research article a grey theory based approach to...

Research ArticleA Grey Theory Based Approach to Big Data RiskManagement Using FMEA

Maisa Mendonccedila Silva1 Thiago Poleto2 Luacutecio Camara e Silva1

Ana Paula Henriques de Gusmao2 and Ana Paula Cabral Seixas Costa2

1Technology Centre Department of Management Engineering Universidade Federal de Pernambuco Rodovia BR 104Km 62 Nova Caruaru 55002-960 Caruaru PE Brazil2School of Engineering Centre for Technology and Geosciences Department of Management EngineeringUniversidade Federal de Pernambuco Caixa Postal 5125 52070-970 Recife PE Brazil

Correspondence should be addressed to Maisa Mendonca Silva maisaufpeyahoocombr

Received 18 March 2016 Revised 3 July 2016 Accepted 26 July 2016

Academic Editor Gang Kou

Copyright copy 2016 Maisa Mendonca Silva et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Big data is the term used to denote enormous sets of data that differ from other classic databases in four main ways (huge) volume(high) velocity (much greater) variety and (big) value In general data are stored in a distributed fashion and on computing nodesas a result of which big data may be more susceptible to attacks by hackers This paper presents a risk model for big data whichcomprises Failure Mode and Effects Analysis (FMEA) and GreyTheory more precisely grey relational analysis This approach hasseveral advantages it provides a structured approach in order to incorporate the impact of big data risk factors it facilitates theassessment of risk by breaking down the overall risk to big data and finally its efficient evaluation criteria can help enterprisesreduce the risks associated with big data In order to illustrate the applicability of our proposal in practice a numerical examplewith realistic data based on expert knowledge was developedThe numerical example analyzes four dimensions that is managingidentification and access registering the device and application managing the infrastructure and data governance and 20 failuremodes concerning the vulnerabilities of big data The results show that the most important aspect of risk to big data relates to datagovernance

1 Introduction

In recent years big data has rapidly developed into an impor-tant topic that has attracted great attention from industry andsociety in general [1]Thebig data concept and its applicationshave emerged from the increasing volumes of external andinternal data in organizations and it differs from otherdatabases in four aspects volume velocity variety and valueVolume refers to the amount of data velocity refers to thespeed with which data can be analyzed and processed varietydescribes the different kinds and sources of data that may bestructured and value refers to valuable discoveries hidden inlarge datasets [2] The emphasis in big data analytics is onhow data is stored in a distributed fashion that allows it tobe processed in parallel on many computing nodes in dis-tributed environments across clusters of machines [3]

Given the significance that big data has for business appli-cations and the increasing interest in various fields relevantworks should bementioned [4] argued that consumer analyt-ics lies at the junction of big data and consumer behavior andhighlights the importance of the interpretation of the datagenerated from big data Reference [5] examined the role ofbig data in facilitating access to financial products for eco-nomically active low-income families and microenterprisesin China Reference [6] investigated the roles of big data andbusiness intelligence (BI) in the decision-making processReference [7] presented a novel active learning methodbased on extreme learningmachines with inherent propertiesthat make handling big data highly attractive Reference [8]developed a selection algorithm based on evolutionary com-putation that uses theMapReduce paradigm to obtain subsetsof features from big datasets Reference [9] discussed the

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016 Article ID 9175418 15 pageshttpdxdoiorg10115520169175418

2 Mathematical Problems in Engineering

advancement of big data technology including the gen-eration management and analysis of data Finally [10]described a brief overview of big data problems includingopportunities and challenges current techniques and tech-nologies

Big data processing begins with data being transmittedfrom different sources to storage devices and continues withthe implementation of preprocessing process mining andanalysis and decision-making [6] Much of this processingtakes place in parallel which increases the risk of attack andhow best to guard against this is what big data managementseeks to do [11]

Over the last few years several researchers have proposedsolutions for mitigating security threats In [12] a taxonomyof events and scenarios was developed and the ranking ofalternatives based on the criticality of the risk was providedbymeans of event tree analysis combined with fuzzy decisiontheory Reference [13] developed a mathematical model tosolve the problem according to the risk management para-digm and thereby providedmanagerswith additional insightsformaking optimal decisionsThere has also been research onthe use of large network traces for mitigating security threats[14]

However research analyzing the risks associated with bigdata is lacking Moreover from this perspective informationsecurity measures are becoming more important due to theincreasingly public nature of multiple sources Hence manyissues related to big data applications can be addressed firstby identifying the possible occurrences of failure and thenby evaluating them Consequently this paper proposes theuse of a specific Failure Mode and Effects Analysis (FMEA)method and GreyTheory which allows for risk assessment atthe crucial stages of the big data process Both mathematicalrigor which is necessary to ensure the robustness of themodel and the judgments of those involved in the processgiven the subjective characteristics of the types of assessmentsmade are considered in this model This paper contributesto the literature in the following aspects First it offers newinsights into how the different characteristics of big data arelinked to risk in information security Second it provides amodel risk analysis based on a multidimensional perspectiveof big data risk analysis

The first section of the paper discusses big data andinformation security issuesThen the discussion that followsrelates to existing methodologies for information securityand background information which are necessary for devel-oping the proposed approach Next we introduce the meth-odology and present a real case that illustrates how the meth-odology validates the proposed approach Finally the discus-sion presents the limitations of the research suggested areasfor further study and concluding remarks

2 Background

21 Big Data and Methodologies for Risk Management Asmentioned before big data has different characteristics interms of variety velocity value and volume compared toclassic databases Consequently big data risk management is

more complex and is becoming one of the greatest concernsin the area of information security Currently another impor-tant point is that data availability and confidentiality are twotop priorities regarding big data

Recently several works relating to big data and securityhave been published Reference [15] proposed a new typeof digital signature that is specifically designed for a graph-based big data system To ensure the security of outsourceddata [16] developed an efficient ID-based auditing protocolfor cloud data integrity using ID-based cryptography Inorder to solve the problem of data integrity [17] proposed aremote data-auditing technique based on algebraic signatureproperties for a cloud storage system that incurs minimalcomputational and communication costs Reference [18] pre-sented a risk assessment process that includes both risk aris-ing from the interference of unauthorized information andissues related to failures in risk-aware access control systems

There are many methods and techniques with respect tobig data risk management Table 1 lists and briefly describesqualitative methodologies for risk analysis

Some approaches based on quantitative methods havealso been proposed Reference [19] presented an approachto the risk management of security information encom-passing FMEA and Fuzzy Theory Reference [20] developedan analysis model to simultaneously define the risk factorsand their causal relationships based on the knowledge fromobserved cases and domain experts Reference [21] proposeda new method called the Information Security Risk AnalysisMethod (ISRAM) based on a quantitative approach

As can be seen the purpose of big data security mech-anisms is to provide protection against malicious partiesHence researchers have also identified several forms ofattacks and vulnerabilities regarding big data Reference [22]investigated key threats that target VoIP hosts Reference[23] analyzed the impact of malicious servers on differenttrust and reputation models in wireless sensor networksReference [24] examined a cloud architecture where differentservices are hosted on virtualized systems on the cloud bymultiple cloud customers Also [25] outlined a discussion ofthe security and privacy challenges of cloud computing

In this context attacks themselves are becoming moreand more sophisticated Moreover attackers also have easieraccess to ready-made tools that enable exploitation of plat-form vulnerabilities more effectively For these reasons thesecurity risks of high volumes of data from multiple sourcescomplex data sharing and accessibility-related issues arisein a big data environment Therefore there is an increasingneed to develop and create new techniques for big data riskanalysis

22 Failure Mode and Effects Analysis (FMEA) FMEA wasfirst proposed by NASA in 1963Themain objective of FMEAis to discover prevent and correct potential failure modesfailure causes failure effects and problem areas affecting asystem [31] According to FMEA the risk priorities of failuremodes are generally determined through the risk priority

Mathematical Problems in Engineering 3

Table 1 Qualitative methodologies for risk analysis

Methods and techniques Description and process AuthorCCTA risk analysis andmanagement method(CRAMM)

Comprises three stages the first two stages identify and analyze the risks to thesystem and the third stage recommends how these risks should be managed [26]

Expert system for securityrisk analysis andmanagement (RAMeX)

Proposes examining the risk assessment portion of the risk management process inseven steps define the problem identify threats determine the probability ofoccurrence identify existing security assess the business impact assess securitycountermeasures and generate report

[27]

Facilitated risk analysisprocess (FRAP)

The process involves analyzing one system of the business operation at a time andconvening a team of individuals who have business information needs and technicalstaff who have a detailed understanding of potential vulnerabilities of the systemand related controls

[28]

Information risk analysismethodologies (IRAM)

Provides three phases first phase conduct a comprehensive assessment of thebusiness impact and determine the business security second phase assess threatand vulnerability of incidents occurring in a system third phase control selection

[29]

Operationally criticalthreat asset andvulnerability evaluation(OCTAVE)

Organized into four phases develop understanding of risk to the business create aprofile of each information asset that establishes clear boundaries and identify itssecurity requirements identify threats to each information asset and mitigate thisrisk

[30]

Table 2 Severity rating scale

Rating Effect Severity of effect

10 Hazardous without warningFailure is hazardous and occurs without warning it suspendsoperation of the system andor involves noncompliance withgovernment regulations

9 Serious Failure involves hazardous outcomes andor noncompliance withgovernment regulations or standards

8 Extreme Big data is inoperable with loss of primary function the system isinoperable

7 High The big data has severely affected performance but functions thesystem may not operate

6 Significant The performance of big data is degraded comfort or conveniencefunctions may not operate

5 Moderate A moderate effect on the performance of big data the productrequires repair

4 Very low A small effect on the performance of big data the product does notrequire repair

3 Minor A minor effect on the performance of the big data or system2 Very minor A very minor effect on the performance of the big data or system1 None No effect

number (RPN) which assesses three factors of risk occur-rence (O) severity (S) and detection (D) Then the RPN isdefined by [32]

RPN = O times S times D (1)

Based on [33 34] the classic proposal uses the 10-pointlinguistic scale for evaluating the O S and D factors Thisscale is described in Tables 2 3 and 4 for each risk factorThe failure modes with higher RPNs which are viewed asmore important should be corrected with higher prioritiesthan those with lower RPNs

TheFMEAmethod has been applied tomany engineeringareas Reference [35] extended the application of FMEA to

risk management in the construction industry using com-bined fuzzy FMEA and fuzzy Analytic Hierarchy Process(AHP) Reference [36] described failures of the fuel feedingsystem that frequently occur in the sugar and pharmaceuticalindustries [37] Reference [38] proposed FMEA for electricpower grids such as solar photovoltaics Reference [39]presented a basis for prioritizing health care problems

According to [40] the traditional FMEA method cannotassign different weightings to the risk factors of O S and Dand therefore may not be suitable for real-world situationsFor these authors introducing GreyTheory to the traditionalFMEA enables engineers to allocate the relative importanceof the risk factors O S and D based on the research and their


Table 3 Occurrence rating scale

Rating Description Potential failure rate10 Certain probability of occurrence Failure occurs at least once a day or almost every time9 Failure is almost inevitable Failure occurs predictably or every three or four days8 Very high probability of occurrence Failure occurs frequently or about once per week76 Moderately high probability of occurrence Failure occurs about once per month54 Moderate probability of occurrence Failure occurs occasionally or once every three months32 Low probability of occurrence Failure occurs rarely or about once per year1 Remote probability of occurrence Failure almost never occurs no one remembers the last failure

Table 4 Detection rating scale

Rating Description Definition10 No chance of detection There is no known mechanism for detecting the failure9 Very remoteunreliable The failure can be detected only with thorough inspection and this is not

feasible or cannot be readily done87 Remote The error can be detected with manual inspection but no process is in

place so detection is left to chance6

5 Moderate chance of detection There is a process for double checks or inspection but it is not automatedandor is applied only to a sample andor relies on vigilance

4 High There is 100 inspection or review of the process but it is not automated32 Very high There is 100 inspection of the process and it is automated1 Almost certain There are automatic ldquoshut-offsrdquo or constraints that prevent failure

experience In general the major advantages of applying thegrey method to FMEA are the following capabilities assign-ing different weightings to each factor and not requiring anytype of utility function [41]

References [32 33] pointed out that the use of GreyTheory within the FMEA framework is practicable and canbe accomplished Reference [42] examined the ability topredict tanker equipment failure Reference [43] proposed anapproach that is expected to help service managers manageservice failuresThus GreyTheory is one approach employedto improve the evaluation of risk

23 Grey Theory Grey Theory introduced by [44] is amethodology that is used to solve uncertainty problemsit allows one to deal with systems that have imperfect orincomplete information or that even lack information GreyTheory comprises grey numbers grey relations (which thispaper uses in the formofGreyRelationalAnalysis GRA) andgrey elements These three essential components are used toreplace classical mathematics [45]

In grey system theory a system with information that iscertain is called a white system a system with informationthat is totally unknown is called a black system a systemwith partially known and partially unknown information iscalled a grey system [46] Reference [47] argued that in recentdays grey system theory is receiving increasing attention

in the field of decision-making and has been successfullyapplied to many important problems featuring uncertaintysuch as supplier selection [48 49] medical diagnosis [50]work safety [40] portfolio selection [51] and classificationalgorithms evaluation and selection [52]

According to [53] a grey system is defined as a systemcontaining uncertain information presented by a grey num-ber and grey variables Another important definition is thatof a grey set 119883 (of a universal set 119880) which is defined by itstwo mappings 120583

119883(119909) and 120583

119883(119909) as follows

120583119883 (119909) 119909 997888rarr [0 1]

120583119883 (119909) 119909 997888rarr [0 1]

(2)

where 120583119883(119909) ge 120583

119883(119909) 119909 isin 119883 119883 = 119877 and 120583

119883(119909) and

120583119883(119909) are the upper and lower membership functions in 119883

respectivelyA grey number is the most fundamental concept in grey

system theory and can be defined as a number with uncertaininformation Therefore a white number is a real number119909 isin R and a grey number written as ⨂119909 refers to anindeterminate real number that takes its possible values fromwithin an interval or a discrete set of numbers In otherwords a grey number ⨂119909 is then defined as an intervalwith a known lower limit and a known upper limit that is as⨂119909 [119909 119909] Supposing there are two different grey numbers


denoted by ⨂1199091and ⨂119909

2 the mathematical operation

rules of general grey numbers are as follows

⨂1199091+⨂119909

2= [1199091+ 1199092 1199091+ 1199092]

⨂1199091minus⨂119909

2= [1199091minus 1199092 1199091+ 1199092]

⨂1199091times⨂119909

2= [min (119909

11199092 11990911199092 11990911199092 11990911199092)

max (11990911199092 11990911199092 11990911199092 11990911199092)]

⨂1199091divide⨂119909

2= [1199091 1199091] times [

1

1199092

1

1199092

]

119896 times⨂1199091= [119896119909 119896119909]

(3)

GRA is a part of Grey Theory and can be used togetherwith various correlated indicators to evaluate and analyze theperformance of complex systems [54 55] In fact GRA hasbeen successfully used in FMEA and its results have beenproven to be satisfactory Compared to other methods GRAhas competitive advantages in terms of having shown theability to process uncertainty and to deal with multi-inputsystems discrete data and data incompleteness effectively[55] In addition [41] argues that results generated by thecombination of Grey Theory and FMEA are more unbiasedthan those of traditional FMEA and [42] claims that com-bining Fuzzy Theory and Grey Theory with FMEA leads tomore useful and practical results

GRA is an impact evaluation model that measures thedegree of similarity or difference between two sequencesbased on the degree of their relationship In GRA a globalcomparison between two sets of data is undertaken instead ofusing a local comparison by measuring the distance betweentwo points [56] Its basic principle is that if a comparabilitysequence translated from an alternative has a higher greyrelational degree between the reference sequence and itselfthen the alternative will be the better choice Thereforethe analytic procedure of GRA normally consists of fourparts generating the grey relational situation defining thereference sequence calculating the grey relational coefficientand finally calculating the grey relational degree [55 57]The comparative sequence denotes the sequences that shouldbe evaluated by GRA and the reference sequence is theoriginal reference that is compared with the comparativesequence Normally the reference sequence is defined as avector consisting of (1 1 1 1) GRA aims to find thealternative that has the comparability sequence that is theclosest to the reference sequence [43]

24 Critical Analysis Big data comprises complex datathat is massively produced and managed in geographicallydispersed repositories [63] Such complexity motivates thedevelopment of advanced management techniques and tech-nologies for dealingwith the challenges of big dataMoreoverhow best to assess the security of big data is an emergingresearch area that has attracted abundant attention in recentyears Existing security approaches carry out checking on

data processing in diverse modes The ultimate goal of theseapproaches is to preserve the integrity and privacy of dataand to undertake computations in single and distributedstorage environments irrespective of the underlying resourcemargins [11]

However as discussed in [11] traditional data securitytechnologies are no longer pertinent to solving big datasecurity problems completely These technologies are unableto provide dynamic monitoring of how data and security areprotected In fact they were developed for static datasets butdata is now changing dynamically [64] Thus it has becomehard to implement effective privacy and security protectionmechanisms that can handle large amounts of data in com-plex circumstances

In a general way FMEA has been extensively used forexamining potential failures in many industries MoreoverFMEA together with Fuzzy Theory andor Grey Theory hasbeen widely and successfully used in the risk management ofinformation systems [12] equipment failure [42] and failurein services [43]

Because the modeling of complex dynamic big datarequires methods that combine human knowledge and expe-rience as well as expert judgment this paper uses GRA toevaluate the level of uncertainty associated with assessing bigdata in the presence or absence of threats It also providesa structured approach in order to incorporate the impact ofrisk factors for big data into a more comprehensive definitionof scenarios with negative outcomes and facilitates the assess-ment of risk by breaking down the overall risk to big dataFinally its efficient evaluation criteria can help enterprisesreduce the risks associated with big data

Therefore from a security and privacy perspective bigdata is different from other traditional data and requires adifferent approach Many of the existing methodologies andpreferred practices cannot be extended to support the bigdata paradigm Big data appears to have similar risks andexposures to traditional data However there are several keyareas where they are dramatically different

In this context variety and volume translate into higherrisks of exposure in the event of a breach due to variability indemand which requires a versatile management platform forstoring processing andmanaging complex data In additionthe new paradigm for big data presents data characteristicsat different levels of granularity and big data projects oftenencompass heterogeneous components Another point ofview states that new types of data are uncovering new privacyimplications with few privacy laws or guidelines to protectthat information

3 The Proposed Model

In this paper an approach to big data risk management usingGRA has been developed to analyze the dimensions that arecritical to big data as described by [65] based on FMEA and[31 32] The approach proposed is presented in Figure 1

The new big data paradigm needs to work with far morethan the traditional subsets of internal data This paradigmincorporates a large volume of unstructured informationlooks for nonobvious correlations that might drive new


FMEA potential failure modes determination and evaluation

(O S and D)

Grey belief and information

decision matrix (x)

Introduction ofthe weights of

risks factors

Determination of the degree of grey relation (for each failure mode and then

for each dimension)

Expert knowledge or use of past data

Compute the grey relational coefficient

Final dimension rank

Comparative series Xn Standard series X0

Obtain differences Δn = Xn minus X0

Figure 1 Flowchart of the proposed FMEA and GreyTheory based approach

hypotheses and must work with data that float into theorganization in real time and that require real-time analysisand response Therefore in this paper we analyzed theprocessing characteristics of the IBM Big Data Platform forillustrative purposes but it is important to note that all bigdata platforms are vulnerable to both external and internalthreats Therefore since our analysis model based on theprobability of the occurrence of failure covers a wide viewof the architecture of big data it is eligible for analyzingother platforms such as cloud computing infrastructures[66] and platforms from business scenarios [67] Finally ourmodel considers the possible occurrence of failures in thedistributed data and then we consider its implementation ina distributed way

31 Expert Knowledge or Past Data regarding Previous Fail-ures Thefirst step in the approach consists of expert identifi-cation or use of past dataThe expert is the personwho knowsthe enterprise systems and their vulnerability and is thus ableto assess the information security risk of the organization interms of the four dimensions [65] One may also identify agroup of experts in this step and the analysis is accomplishedby considering a composition of their judgments or the useof a dataset of past failuresThe inclusion of an expert systemin the model is also encouraged

According to [68] an expert is someone with multipleskills who understands the working environment and hassubstantial training in and knowledge of the system beingevaluated Risk management models have widely used expertknowledge to provide value judgments that represent theexpertrsquos perceptions andor preferences For instance [69]provides evidence obtained from two unbiased and inde-pendent experts regarding the risk of release of a highlyflammable gas near a processing facility References [70 71]explore a risk measure of underground vaults that considersthe consequences of arc faults using a single expertrsquos a prioriknowledge Reference [19] proposes information securityrisk management using FMEA Fuzzy Theory and expertknowledge Reference [72] analyzes the risk probability of anunderwater tunnel excavation using the knowledge of fourexperts

32 Determination and Evaluation of Potential Failure Modes(FMEA) In a general way this step concerns the determi-nation of the failure modes associated with the big datadimensions (Figure 2) in terms of their vulnerabilities Eachdimension is described in Table 5

Furthermore these dimensions can be damaged by var-ious associated activities Table 6 presents failure modesrelating to the vulnerability of big data for each dimension


Table 5 Description of dimensions

Dimension Description

Identification and access management

Given the opportunity to increase knowledge by accessing big data it is necessarythat only authorized persons can access it thus big data requires confidentiality andauthenticity to address this problem [58] mentioned that sometimes both areneeded simultaneously this source recommended and proposed three differentschemes an encryption scheme a signature scheme and a sign-encryption scheme

Device and application registration

Data provenance refers to information about the history of a creation process inother words it refers to a mechanism that can be used to validate whether inputdata is coming from an authenticated source to guarantee a degree of informationintegrity [59] then provenance-related security and trustworthiness issues alsoarise in the system [60] they include the registration of devices inmachine-to-machine (M2M) and Internet-of-Things (IoT) networks which can beconsidered one of the major issues in the area of security [61]

Infrastructure management

As big data physical infrastructures increase difficulties associated with designingeffective physical security also arise thus we use the term ldquosystem healthrdquo todescribe the intersection of the information worker and the nominal conditions forinfrastructure management monitoring of big data for security purposes whichinclude technical issues regarding the interoperability of services [62]

Data governanceData governance can ensure appropriate controls without inhibiting the speed andflexibility of innovative big data approaches and technologies which need to beestablished for different management levels with a clear security strategy

Big data security


Data governanceInfrastructure management


Figure 2 Big data dimensions

In fact the determination of the failuremodes is achievedusing the FMEA methodology and evaluated regarding itsoccurrence (O) severity (S) and detection (D)

33 Establish Comparative Series An information series with119899 decision factors such as chance of occurrence severity offailure or chance of lack of detection can be expressed asfollows

119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)

These comparative series can be provided by an expert or anydataset of previous failures based on the scales described inTables 2ndash4

34 Establish the Standard Series According to [41] thedegree of relation can describe the relationship of twoseries thus an objective series called the standard series isestablished and expressed as 119883

0= (1198830(1) 119883

0(2) 119883

0(119896))

where 119896 is the number of risk factors (for this work 119896 = 3 ieoccurrence severity and detection) According to FMEA as

the score becomes smaller the standard series can be denotedas1198830= (1198830(1) 119883

0(2) 119883

0(119896)) = (1 1 1)

35 Obtain the Difference between the Comparative Seriesand the Standard Series To discover the degree of thegrey relationship the difference between the score of thedecision factors and the norm of the standard series must bedetermined and expressed by a matrix calculated by

Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)

where 119895 is the number of failure modes in the analysis [31]

36 Compute the Grey Relational Coefficient The grey rela-tional coefficient is calculated by

120574 (1198830 (119896) 119883119895 (

119896)) =

Δmin minus 120577ΔmaxΔ0119895 (

119896) minus 120577Δmax (6)

where 120577 is an identifier normally set to 05 [31] It only affectsthe relative value of risk not the priority


Table 6 Failure modes associated with each dimension of big data

Dimensions Associated activities

A1 Identification and access management

A11 Loss of secret keysA12 Cryptanalysis of a ciphered signalA13 Secret password divulged to any other userA14 Intentional access to network services for example proxy serversA15 Spoofing impersonation of a legitimate user

A2 Device and application registration

A21 Facility problemsA22 Failure of encryption equipmentA23 Unauthorized use of secure equipmentA24 Ineffective infrastructure investmentA25 Failure of application server

A3 Infrastructure management

A31 Cabling problemsA32 Failure of radio platform transmissionA33 Failure of cipher audio (telephone) and videoA34 Failure of sensor networksA35 Failure of potential of energyA36 Unauthorized readout of data stored on a remote LAN

A4 Data governance

A41 Failure of interpretation and analysis of dataA42 Failure of audit review of implemented policies and information securityA43 Failure to maximize new business valueA44 Failure of real-time demand forecasts

37 Determine the Degree of Relation Before finding thedegree of relation the relative weight of the decision factorsis first decided so that it can be used in the followingformulation [31] In a general way it is calculated by

Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)

where 120573119896is the risk factorsrsquo weighting and as a result

sum

119899

119896=1120573119896= 1

38 Rank the Priority of Risk This step consists of dimensionordering Based on the degree of relation between thecomparative series and the standard series a relational seriescan be constructed The greater the degree of relation thesmaller the effect of the cause [31]

4 An Illustrative Example

To demonstrate the applicability of our proposition based onFMEA and Grey Theory an example based on a real contextis presented in this section The steps performed are thesame as shown in Figure 1 explained in Section 3 Followingthese steps the expert selected for this study is a senioracademic with more than 20 yearsrsquo experience She holds aPhD degree in information systems (IS) has published 12papers in this field and also has experience as a consultant inIS to companies in the private sector

In the following step of the proposed model the fourdimensions associated with the potential failures of big data

are represented according to Figure 2 and described inTable 5 Furthermore Table 6 presents the failure modesrelating to the vulnerability of big data for each dimensionBased on these potential failures Tables 7 and 8 showthe establishment of comparative and standard series foroccurrence severity and detection respectively

To proceed to a grey relational analysis of potentialaccidents it is necessary to obtain the difference betweencomparative series and standard series according to (4)Table 9 shows the result of this difference

In order to rank the priority of risk it is necessary tocompute both the grey relational coefficient (Table 10) and thedegree of relation (Table 11) using (5) (6) and (7) Thereforethe greater the degree of relation the smaller the effect of thecause Assuming equal weights for risk factors Table 11 alsopresents the degree of grey relation for each failure mode anddimension and final ranking

From the analysis of failures using the proposedapproach we have shown that big data is mainly in needof structured policies for data governance This result wasexpected because the veracity and provenance of data arefundamental to information security otherwise the vulner-abilities may be catastrophic or big data may have little valuefor the acquisition of knowledge Data governance is also anaspect that requires more awareness because it deals withlarge amounts of data and directly influences operationalcosts

Since the model works with a recommendation ratherthan a solution and compatible recommendations depend onexpert knowledge it is important to test the robustness of


Table 7 Comparative series

Dimensions Associated activities O S D


A11 Loss of secret keys 5 7 4A12 Cryptanalysis of a ciphered signal 5 5 4A13 Secret password divulged to any other user 2 7 5A14 Intentional access to network services for example proxy servers 6 5 7A15 Spoofing impersonation of a legitimate user 6 5 7


A21 Facility problems 8 7 5A22 Failure of encryption equipment 6 9 5A23 Unauthorized use of secure equipment 6 5 4A24 Ineffective infrastructure investment 8 5 4A25 Failure of application server 5 4 5


A31 Cabling problems 6 5 4A32 Failure of radio platform transmission 2 9 4A33 Failure of cipher audio (telephone) and video 2 7 4A34 Failure of sensor networks 5 7 2A35 Failure of potential of energy 2 7 2A36 Unauthorized readout of data stored on a remote LAN 5 5 4

A4 Data governance

A41 Failure of interpretation and analysis of data 8 9 5A42 Failure of audit review of implemented policies and information security 8 9 4A43 Failure to maximize new business value 8 7 5A44 Failure of real-time demand forecasts 8 7 7

Table 8 Standard series








A4 Data governance


this information and therefore to conduct sensitivity analysisThus different weightings based on the context may also beused for different risk factors as suggested by [33] Table 12presents a sensitivity analysis conducted in order to evaluatethe performance and validity of the results of the model Ascan be seen the final ranking of risk is the same for all thedifferent weightings tested (plusmn10)

5 Discussion and Conclusions

Themain difficulties in big data security risk analysis involvethe volume of data and the variety of data connected todifferent databases From the perspective of security andprivacy traditional databases have governance controls anda consolidated auditing process while big data is at an early


Table 9 Difference between comparative series and standard series








A4 Data governance


stage of development and hence continues to require struc-tured analysis to address threats and vulnerabilities More-over there is not yet enough research into risk analysis in thecontext of big data

Thus security is one of the most important issues for thestability and development of big data Aiming to identify therisk factors and the uncertainty associated with the prop-agation of vulnerabilities this paper proposed a systematicframework based on FMEA and GreyTheory more preciselyGRA This systematic framework allows for an evaluationof risk factors and their relative weightings in a linguisticas opposed to a precise manner for evaluation of big datafailure modes This is in line with the uncertain nature ofthe context In fact according to [40] the traditional FMEAmethod cannot assign different weightings to the risk factorsofO S andD and thereforemay not be suitable for real-worldsituations These authors pointed out that introducing GreyTheory into the traditional FMEA method enables engineersto allocate relative importance to the O S and D risk factorsbased on research and their own experience In a general wayanother advantage of this proposal is that it requires less efforton the part of experts using linguistic terms Consequentlythese experts can make accurate judgments using linguisticterms based on their experience or on datasets relating toprevious failures

Based on the above information the use of our proposalis justified to identify and assess big data risk in a quantitativemanner Moreover this study comprises various securitycharacteristics of big data using FMEA it analyzes fourdimensions identification and access management deviceand application registration infrastructuremanagement anddata governance as well as 20 subdimensions that represent

failure modes Therefore this work can be expected to serveas a guideline for managing big data failures in practice

It is worth stating that the results presented greater aware-ness of data governance for ensuring appropriate controlsIn this context a challenge to the process of governingbig data is to categorize model and map data as it iscaptured and stored mainly because of the unstructurednature of the volume of information Then one role of datagovernance in the information security context is to allow forthe information that contributes to reporting to be definedconsistently across the organization in order to guide andstructure the most important activities and to help clarifydecisions Briefly analyzing data from the distant past todecide on a current situation does not mean that the data hashigher value From another perspective increasing volumedoes not guarantee confidence in decisions and one may usetools such as datamining and knowledge discovery proposedin [73] to improve the decision process

Indeed the concept of storage management is a criticalpoint especially when volumes of data that exceed the storagecapacity are considered [11] In fact the emphasis of big dataanalytics is on how data is stored in a distributed fashionfor example in traditional databases or in a cloud [66]When a cloud is used data can be processed in parallel onmany computing nodes in distributed environments acrossclusters ofmachines [3] In conclusion big data securitymustbe seen as an important and challenging feature capableof generating significant limitations For instance severalelectronic devices that enable communication via networksespecially via the Internet and which place great emphasison mobile trends allow for an increase in volume varietyand even speed of data which can thereby be defined as big


Table10G

reyrelationalcoefficient

Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio

nalaccesstonetworkservicesfor

exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea

ndapplicationregistratio

n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv

einfrastructureinvestm

ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848


Table 12 Sensitivity analysis

Weights of risk factors Degree of grey relation(dimension) and risk ranking

Occurrence 030Severity 035Detection 035

D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)

data content This fact adds more value to large volumes ofdata and allows for the support of organizational activitiesbequeathing even more importance to the area of dataprocessing which now tends to work in a connected way thatgoes beyond the boundaries of companies

This research contributes as a guide for researchers in theanalysis of suitable big data risk techniques and in the devel-opment of response to the insufficiency of existing solutionsThis risk model can ensure the identification of failure andattacks and help the victim decide how to react when thistype of attack occurs However this study has limitationsFor instance it does not measure the consequences of adisaster occurring in the field of big data This measurementcould be carried out based on [74] Future work shouldfocus on developing a model to ensure the working of datagovernance and should recommend specific actions to ensurethe safety of big data and to help managers choose the bestsafeguards to reduce risks Further studies may also considersecurity-related issues in the fields of enterprise architectureinformation infrastructure and cloud-based computing

Competing Interests

The authors declare that they have no competing interests

Acknowledgments

This research was partially supported by Universidade Fed-eral de Pernambuco and GPSID Decision and InformationSystems Research Group

References

[1] R Tinati S Halford L Carr and C Pope ldquoBig data method-ological challenges and approaches for sociological analysisrdquoSociology vol 48 no 4 pp 663ndash681 2014

[2] M Chen S Mao and Y Liu ldquoBig data a surveyrdquo Mobile Net-works and Applications vol 19 no 2 pp 171ndash209 2014

[3] H Hu Y Wen T-S Chua and X Li ldquoToward scalable systemsfor big data analytics a technology tutorialrdquo IEEE Access vol 2pp 652ndash687 2014

[4] S Erevelles N Fukawa and L Swayne ldquoBig Data consumeranalytics and the transformation of marketingrdquo Journal ofBusiness Research vol 69 no 2 pp 897ndash904 2016

[5] N Kshetri ldquoBig datarsquos role in expanding access to financialservices inChinardquo International Journal of InformationManage-ment vol 36 no 3 pp 297ndash308 2016

[6] T Poleto V D H de Carvalho and A P C S Costa ldquoTheroles of big data in the decision-support process an empiricalinvestigationrdquo inDecision Support Systems VmdashBig Data Analyt-ics for Decision Making First International Conference ICDSST2015 Belgrade Serbia May 27ndash29 2015 Proceedings vol 216of Lecture Notes in Business Information Processing pp 10ndash21Springer Berlin Germany 2015

[7] E G Horta C L de Castro and A P Braga ldquoStream-basedextreme learning machine approach for big data problemsrdquoMathematical Problems in Engineering vol 2015 Article ID126452 17 pages 2015

[8] D Peralta S del Rıo S Ramırez-Gallego I Triguero J MBenitez and F Herrera ldquoEvolutionary feature selection forbig data classification a MapReduce approachrdquo MathematicalProblems in Engineering vol 2015 Article ID 246139 11 pages2015

[9] X Song YWu YMa Y Cui andGGong ldquoMilitary simulationbig data background state of the art and challengesrdquo Mathe-matical Problems in Engineering vol 2015 Article ID 29835620 pages 2015

[10] C L Philip Chen and C-Y Zhang ldquoData-intensive applica-tions challenges techniques and technologies a survey on BigDatardquo Information Sciences vol 275 pp 314ndash347 2014

[11] A Siddiqa I A T Hashem I Yaqoob et al ldquoA survey of bigdata management taxonomy and state-of-the-artrdquo Journal ofNetwork and Computer Applications vol 71 pp 151ndash166 2016

[12] A P H De Gusmao L C E Silva M M Silva T Poleto and AP C S Costa ldquoInformation security risk analysis model usingfuzzy decision theoryrdquo International Journal of InformationManagement vol 36 no 1 pp 25ndash34 2016

[13] W T Yue M Cakanyildirim Y U Ryu and D Liu ldquoNetworkexternalities layered protection and IT security risk manage-mentrdquo Decision Support Systems vol 44 no 1 pp 1ndash16 2007

[14] K Singh S C Guntuku A Thakur and C Hota ldquoBig DataAnalytics framework for Peer-to-Peer Botnet detection usingRandom Forestsrdquo Information Sciences vol 278 pp 488ndash4972014

[15] S Hou X Huang J K Liu J Li and L Xu ldquoUniversal desig-nated verifier transitive signatures for graph-based big datardquoInformation Sciences vol 318 pp 144ndash156 2015


[16] J Zhang and Q Dong ldquoEfficient ID-based public auditing forthe outsourced data in cloud storagerdquo Information Sciences vol343-344 pp 1ndash14 2016

[17] M Sookhak A Gani M K Khan and R Buyya ldquoDynamicremote data auditing for securing big data storage in cloudcomputingrdquo Information Sciences 2015

[18] N Baracaldo and J Joshi ldquoAn adaptive risk managementand access control framework to mitigate insider threatsrdquoComputers and Security vol 39 pp 237ndash254 2013

[19] M M Silva A P H de Gusmao T Poleto L C E Silva andA P C S Costa ldquoA multidimensional approach to informationsecurity risk management using FMEA and fuzzy theoryrdquoInternational Journal of Information Management vol 34 no6 pp 733ndash740 2014

[20] N Feng H J Wang and M Li ldquoA security risk analysis modelfor information systems causal relationships of risk factors andvulnerability propagation analysisrdquo Information Sciences vol256 no 20 pp 57ndash73 2014

[21] B Karabacak and I Sogukpinar ldquoISRAM information securityrisk analysis methodrdquoComputers and Security vol 24 no 2 pp147ndash159 2005

[22] R Farley and X Wang ldquoExploiting VoIP softphone vulner-abilities to disable host computers attacks and mitigationrdquoInternational Journal of Critical Infrastructure Protection vol 7no 3 pp 141ndash154 2014

[23] V K Verma S Singh and N P Pathak ldquoImpact of maliciousservers over trust and reputation models in wireless sensornetworksrdquo International Journal of Electronics vol 103 no 3 pp530ndash540 2016

[24] V Varadharajan and U Tupakula ldquoCounteracting securityattacks in virtual machines in the cloud using property basedattestationrdquo Journal of Network and Computer Applications vol40 no 1 pp 31ndash45 2014

[25] H Takabi J B D Joshi and G-J Ahn ldquoSecurity and privacychallenges in cloud computing environmentsrdquo IEEE Securityand Privacy vol 8 no 6 pp 24ndash31 2010

[26] SANS ldquoA Qualitative Risk Analysis and Management Tool-CRAMMrdquo 2002

[27] M P Kailay and P Jarratt ldquoRAMeX a prototype expertsystem for computer security risk analysis and managementrdquoComputers amp Security vol 14 no 5 pp 449ndash463 1995

[28] T R Peltier Facilitated Risk Analysis Process (FRAP) AuerbachPublications 2000

[29] J Creasey ldquoA complete information risk management solutionFor ISF members using IRAM and STREAMrdquo in ManagingInformation Risk pp 1ndash7 2013

[30] CAlberts andADorofeeManaging Information Security RisksThe OCTAVE Approach Addison-Wesley 2002

[31] R J Mikulak R McDermott and M BeauregardThe Basics ofFMEA vol 2 CRC Press Boca Raton Fla USA 2009

[32] A Pillay and J Wang ldquoModified failure mode and effectsanalysis using approximate reasoningrdquo Reliability Engineeringand System Safety vol 79 no 1 pp 69ndash85 2003

[33] M Ben Daya and Abdul Raouf ldquoA revised failure mode andeffects analysis modelrdquo International Journal of Quality ampReliability Management vol 13 no 1 pp 43ndash47 1996

[34] J B Bowles and C E Pelaez ldquoFuzzy logic prioritization offailures in a system failuremode effects and criticality analysisrdquoReliability Engineering and System Safety vol 50 no 2 pp 203ndash213 1995

[35] M Abdelgawad and A R Fayek ldquoRisk management in theconstruction industry using combined fuzzy FMEA and fuzzyAHPrdquo Journal of Construction Engineering and Managementvol 136 no 9 pp 1028ndash1036 2010

[36] AMariajayaprakash and T Senthilvelan ldquoFailure detection andoptimization of sugar mill boiler using FMEA and Taguchimethodrdquo Engineering Failure Analysis vol 30 pp 17ndash26 2013

[37] O Kaljevic J Djuris Z Djuric and S Ibric ldquoApplication of fail-ure mode and effects analysis in quality by design approach forformulation of carvedilol compression coated tabletsrdquo Journal ofDrug Delivery Science and Technology vol 32 pp 56ndash63 2016

[38] A Colli ldquoFailure mode and effect analysis for photovoltaicsystemsrdquoRenewable and Sustainable Energy Reviews vol 50 pp804ndash809 2015

[39] C Kahraman I Kaya and O Senvar ldquoHealthcare failure modeand effects analysis under fuzzinessrdquoHuman andEcological RiskAssessment vol 19 no 2 pp 538ndash552 2013

[40] J Wei L Zhou F Wang and D Wu ldquoWork safety evaluationin Mainland China using grey theoryrdquo Applied MathematicalModelling vol 39 no 2 pp 924ndash933 2015

[41] C-L Chang P-H Liu andC-CWei ldquoFailuremode and effectsanalysis using grey theoryrdquo Integrated Manufacturing Systemsvol 12 no 3 pp 211ndash216 2001

[42] Q Zhou andV VThai ldquoFuzzy and grey theories in failuremodeand effect analysis for tanker equipment failure predictionrdquoSafety Science vol 83 pp 74ndash79 2016

[43] Y Geum Y Cho and Y Park ldquoA systematic approach fordiagnosing service failure service-specific FMEA and greyrelational analysis approachrdquo Mathematical and ComputerModelling vol 54 no 11-12 pp 3126ndash3142 2011

[44] J-L Deng ldquoControl problems of grey systemsrdquo Systems ampControl Letters vol 1 no 5 pp 288ndash294 1982

[45] J L Deng ldquoIntroduction to grey system theoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[46] H Kuang M A Bashar KW Hipel and D M Kilgour ldquoGrey-based preference in a graph model for conflict resolution withmultiple decision makersrdquo IEEE Transactions on Systems Manand Cybernetics Systems vol 45 no 9 pp 1254ndash1267 2015

[47] H Kuang D M Kilgour and K W Hipel ldquoGrey-basedPROMETHEE II with application to evaluation of source waterprotection strategiesrdquo Information Sciences vol 294 pp 376ndash389 2015

[48] M S Memon Y H Lee and S I Mari ldquoGroup multi-criteriasupplier selection using combined grey systems theory anduncertainty theoryrdquo Expert Systems with Applications vol 42no 21 pp 7951ndash7959 2015

[49] D Golmohammadi and M Mellat-Parast ldquoDeveloping a grey-based decision-making model for supplier selectionrdquo Interna-tional Journal of Production Economics vol 137 no 2 pp 191ndash200 2012

[50] Z Li G Wen and N Xie ldquoAn approach to fuzzy soft setsin decision making based on grey relational analysis andDempster-Shafer theory of evidence an application in medicaldiagnosisrdquo Artificial Intelligence in Medicine vol 64 no 3 pp161ndash171 2015

[51] R Bhattacharyya ldquoA grey theory based multiple attributeapproach for RampD project portfolio selectionrdquo Fuzzy Informa-tion and Engineering vol 7 no 2 pp 211ndash225 2015

[52] G Kou Y Lu Y Peng and Y Shi ldquoEvaluation of classificationalgorithms using MCDM and rank correlationrdquo InternationalJournal of Information Technology and Decision Making vol 11no 1 pp 197ndash225 2012


[53] G-D Li D Yamaguchi and M Nagai ldquoA grey-based decision-making approach to the supplier selection problemrdquoMathemat-ical and Computer Modelling vol 46 no 3-4 pp 573ndash581 2007

[54] H-HWu ldquoA comparative study of using grey relational analysisin multiple attribute decision making problemsrdquo Quality Engi-neering vol 15 no 2 pp 209ndash217 2002

[55] Y Kuo T Yang and G-W Huang ldquoThe use of grey relationalanalysis in solving multiple attribute decision-making prob-lemsrdquo Computers and Industrial Engineering vol 55 no 1 pp80ndash93 2008

[56] W-S Lee and Y-C Lin ldquoEvaluating and ranking energyperformance of office buildings using Grey relational analysisrdquoEnergy vol 36 no 5 pp 2551ndash2556 2011

[57] C-L Chang C-C Wei and Y-H Lee ldquoFailure mode andeffects analysis using fuzzymethod and grey theoryrdquoKybernetesvol 28 no 8-9 pp 1072ndash1080 1999

[58] G Wei J Shao Y Xiang P Zhu and R Lu ldquoObtain confiden-tiality orand authenticity in big data by ID-based generalizedsigncryptionrdquo Information Sciences vol 318 pp 111ndash122 2015

[59] B Glavic ldquoBig data provenance challenges and implications forbenchmarkingrdquo in Specifying Big Data Benchmarks pp 72ndash802014

[60] J Park D Nguyen and R Sandhu ldquoA provenance-based accesscontrol modelrdquo in Proceedings of the 10th Annual InternationalConference on Privacy Security and Trust (PST rsquo12) pp 137ndash144Paris France July 2012

[61] H-C Chen I You C-E Weng C-H Cheng and Y-FHuang ldquoA security gateway application for End-to-End M2Mcommunicationsrdquo Computer Standards and Interfaces vol 44pp 85ndash93 2016

[62] R A Oliveira N Laranjeiro and M Vieira ldquoAssessing thesecurity of web service frameworks against Denial of Serviceattacksrdquo Journal of Systems and Software vol 109 pp 18ndash312015

[63] K Kambatla G Kollias V Kumar andAGrama ldquoTrends in bigdata analyticsrdquo Journal of Parallel and Distributed Computingvol 74 no 7 pp 2561ndash2573 2014

[64] G Lafuente ldquoThe big data security challengerdquoNetwork Securityvol 2015 no 1 pp 12ndash14 2015

[65] National Institute of Standards and TechnologymdashNIST BigData Interoperability Framework Security and Privacy vol 4NIST Gaithersburg Md USA 2015

[66] R Iqbal F Doctor B More S Mahmud and U Yousuf ldquoBigdata analytics computational intelligence techniques and appli-cation areasrdquo International Journal of InformationManagement2016

[67] J Chen Y Tao H Wang and T Chen ldquoBig data based fraudrisk management at Alibabardquo The Journal of Finance and DataScience vol 1 no 1 pp 1ndash10 2015

[68] J H Purba ldquoA fuzzy-based reliability approach to evaluate basicevents of fault tree analysis for nuclear power plant probabilisticsafety assessmentrdquo Annals of Nuclear Energy vol 70 pp 21ndash292014

[69] R Ferdous F Khan R Sadiq P Amyotte and B VeitchldquoHandling data uncertainties in event tree analysisrdquo ProcessSafety and Environmental Protection vol 87 no 5 pp 283ndash2922009

[70] T V Garcez and A T De Almeida ldquoMultidimensional riskassessment of manhole events as a decision tool for ranking thevaults of an underground electricity distribution systemrdquo IEEETransactions on Power Delivery vol 29 no 2 pp 624ndash632 2014

[71] T V Garcez andA T DeAlmeida ldquoA riskmeasurement tool foran underground electricity distribution system considering theconsequences and uncertainties of manhole eventsrdquo ReliabilityEngineering and System Safety vol 124 pp 68ndash80 2014

[72] E-S Hong I-M Lee H-S Shin S-W Nam and J-S KongldquoQuantitative risk evaluation based on event tree analysistechnique application to the design of shield TBMrdquo Tunnellingand Underground Space Technology vol 24 no 3 pp 269ndash2772009

[73] Y Peng G Kou Y Shi and Z Chen ldquoA descriptive frameworkfor the field of data mining and knowledge discoveryrdquo Interna-tional Journal of Information Technology and Decision Makingvol 7 no 4 pp 639ndash682 2008

[74] D Feledi and S Fenz ldquoChallenges of web-based informationsecurity knowledge sharingrdquo in Proceedings of the 7th Interna-tional Conference on Availability Reliability and Security (ARESrsquo12) pp 514ndash521 Prague Czech Republic August 2012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


advancement of big data technology including the gen-eration management and analysis of data Finally [10]described a brief overview of big data problems includingopportunities and challenges current techniques and tech-nologies

Big data processing begins with data being transmittedfrom different sources to storage devices and continues withthe implementation of preprocessing process mining andanalysis and decision-making [6] Much of this processingtakes place in parallel which increases the risk of attack andhow best to guard against this is what big data managementseeks to do [11]

Over the last few years several researchers have proposedsolutions for mitigating security threats In [12] a taxonomyof events and scenarios was developed and the ranking ofalternatives based on the criticality of the risk was providedbymeans of event tree analysis combined with fuzzy decisiontheory Reference [13] developed a mathematical model tosolve the problem according to the risk management para-digm and thereby providedmanagerswith additional insightsformaking optimal decisionsThere has also been research onthe use of large network traces for mitigating security threats[14]

However research analyzing the risks associated with bigdata is lacking Moreover from this perspective informationsecurity measures are becoming more important due to theincreasingly public nature of multiple sources Hence manyissues related to big data applications can be addressed firstby identifying the possible occurrences of failure and thenby evaluating them Consequently this paper proposes theuse of a specific Failure Mode and Effects Analysis (FMEA)method and GreyTheory which allows for risk assessment atthe crucial stages of the big data process Both mathematicalrigor which is necessary to ensure the robustness of themodel and the judgments of those involved in the processgiven the subjective characteristics of the types of assessmentsmade are considered in this model This paper contributesto the literature in the following aspects First it offers newinsights into how the different characteristics of big data arelinked to risk in information security Second it provides amodel risk analysis based on a multidimensional perspectiveof big data risk analysis

The first section of the paper discusses big data andinformation security issuesThen the discussion that followsrelates to existing methodologies for information securityand background information which are necessary for devel-oping the proposed approach Next we introduce the meth-odology and present a real case that illustrates how the meth-odology validates the proposed approach Finally the discus-sion presents the limitations of the research suggested areasfor further study and concluding remarks

2 Background

21 Big Data and Methodologies for Risk Management Asmentioned before big data has different characteristics interms of variety velocity value and volume compared toclassic databases Consequently big data risk management is

more complex and is becoming one of the greatest concernsin the area of information security Currently another impor-tant point is that data availability and confidentiality are twotop priorities regarding big data

Recently several works relating to big data and securityhave been published Reference [15] proposed a new typeof digital signature that is specifically designed for a graph-based big data system To ensure the security of outsourceddata [16] developed an efficient ID-based auditing protocolfor cloud data integrity using ID-based cryptography Inorder to solve the problem of data integrity [17] proposed aremote data-auditing technique based on algebraic signatureproperties for a cloud storage system that incurs minimalcomputational and communication costs Reference [18] pre-sented a risk assessment process that includes both risk aris-ing from the interference of unauthorized information andissues related to failures in risk-aware access control systems

There are many methods and techniques with respect tobig data risk management Table 1 lists and briefly describesqualitative methodologies for risk analysis

Some approaches based on quantitative methods havealso been proposed Reference [19] presented an approachto the risk management of security information encom-passing FMEA and Fuzzy Theory Reference [20] developedan analysis model to simultaneously define the risk factorsand their causal relationships based on the knowledge fromobserved cases and domain experts Reference [21] proposeda new method called the Information Security Risk AnalysisMethod (ISRAM) based on a quantitative approach

As can be seen the purpose of big data security mech-anisms is to provide protection against malicious partiesHence researchers have also identified several forms ofattacks and vulnerabilities regarding big data Reference [22]investigated key threats that target VoIP hosts Reference[23] analyzed the impact of malicious servers on differenttrust and reputation models in wireless sensor networksReference [24] examined a cloud architecture where differentservices are hosted on virtualized systems on the cloud bymultiple cloud customers Also [25] outlined a discussion ofthe security and privacy challenges of cloud computing

In this context attacks themselves are becoming moreand more sophisticated Moreover attackers also have easieraccess to ready-made tools that enable exploitation of plat-form vulnerabilities more effectively For these reasons thesecurity risks of high volumes of data from multiple sourcescomplex data sharing and accessibility-related issues arisein a big data environment Therefore there is an increasingneed to develop and create new techniques for big data riskanalysis

22 Failure Mode and Effects Analysis (FMEA) FMEA wasfirst proposed by NASA in 1963Themain objective of FMEAis to discover prevent and correct potential failure modesfailure causes failure effects and problem areas affecting asystem [31] According to FMEA the risk priorities of failuremodes are generally determined through the risk priority







[27]



[28]



[29]



[30]
































119883(119909) and 120583

119883(119909) as follows

120583119883 (119909) 119909 997888rarr [0 1]

120583119883 (119909) 119909 997888rarr [0 1]

(2)

where 120583119883(119909) ge 120583

119883(119909) 119909 isin 119883 119883 = 119877 and 120583

119883(119909) and








⨂1199091+⨂119909

2= [1199091+ 1199092 1199091+ 1199092]

⨂1199091minus⨂119909

2= [1199091minus 1199092 1199091+ 1199092]

⨂1199091times⨂119909

2= [min (119909

11199092 11990911199092 11990911199092 11990911199092)

max (11990911199092 11990911199092 11990911199092 11990911199092)]

⨂1199091divide⨂119909

2= [1199091 1199091] times [

1

1199092

1

1199092

]

119896 times⨂1199091= [119896119909 119896119909]

(3)















(O S and D)


decision matrix (x)


risks factors


for each dimension)






















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra















[27]



[28]



[29]



[30]
































119883(119909) and 120583

119883(119909) as follows

120583119883 (119909) 119909 997888rarr [0 1]

120583119883 (119909) 119909 997888rarr [0 1]

(2)

where 120583119883(119909) ge 120583

119883(119909) 119909 isin 119883 119883 = 119877 and 120583

119883(119909) and








⨂1199091+⨂119909

2= [1199091+ 1199092 1199091+ 1199092]

⨂1199091minus⨂119909

2= [1199091minus 1199092 1199091+ 1199092]

⨂1199091times⨂119909

2= [min (119909

11199092 11990911199092 11990911199092 11990911199092)

max (11990911199092 11990911199092 11990911199092 11990911199092)]

⨂1199091divide⨂119909

2= [1199091 1199091] times [

1

1199092

1

1199092

]

119896 times⨂1199091= [119896119909 119896119909]

(3)















(O S and D)


decision matrix (x)


risks factors


for each dimension)






















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
























119883(119909) and 120583

119883(119909) as follows

120583119883 (119909) 119909 997888rarr [0 1]

120583119883 (119909) 119909 997888rarr [0 1]

(2)

where 120583119883(119909) ge 120583

119883(119909) 119909 isin 119883 119883 = 119877 and 120583

119883(119909) and








⨂1199091+⨂119909

2= [1199091+ 1199092 1199091+ 1199092]

⨂1199091minus⨂119909

2= [1199091minus 1199092 1199091+ 1199092]

⨂1199091times⨂119909

2= [min (119909

11199092 11990911199092 11990911199092 11990911199092)

max (11990911199092 11990911199092 11990911199092 11990911199092)]

⨂1199091divide⨂119909

2= [1199091 1199091] times [

1

1199092

1

1199092

]

119896 times⨂1199091= [119896119909 119896119909]

(3)















(O S and D)


decision matrix (x)


risks factors


for each dimension)






















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













⨂1199091+⨂119909

2= [1199091+ 1199092 1199091+ 1199092]

⨂1199091minus⨂119909

2= [1199091minus 1199092 1199091+ 1199092]

⨂1199091times⨂119909

2= [min (119909

11199092 11990911199092 11990911199092 11990911199092)

max (11990911199092 11990911199092 11990911199092 11990911199092)]

⨂1199091divide⨂119909

2= [1199091 1199091] times [

1

1199092

1

1199092

]

119896 times⨂1199091= [119896119909 119896119909]

(3)















(O S and D)


decision matrix (x)


risks factors


for each dimension)






















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











(O S and D)


decision matrix (x)


risks factors


for each dimension)






















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra



















Big data security







119883119894= (119883119894 (1) 119883119894 (

2) 119883119894 (119896)) (4)



0= (1198830(1) 119883

0(2) 119883

0(119896))



0(2) 119883

0(119896)) = (1 1 1)


Δ0119895 (

119896) =

10038171003817100381710038171003817

1198830 (119896) minus 119883119895 (

119896)

10038171003817100381710038171003817

(5)



120574 (1198830 (119896) 119883119895 (

119896)) =


119896) minus 120577Δmax (6)











A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















A4 Data governance



Γ (119883119894 119883119895) =

119899

sum

119896=1

120573119896120574 (119883119894 (119896) 119883119895 (

119896)) (7)


sum

119899

119896=1120573119896= 1



















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















A4 Data governance










A4 Data governance














A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

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arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

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entedpo

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ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra


















A4 Data governance









Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Table10G


Dim

ensio

nsAs

sociated

activ

ities

OS

D

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0625

05

0714286

A12

Cryptanalysisof

acipheredsig

nal

0625

0625

0714286

A13

Secretp

assw

orddivulged

toanyotheru

ser

105

0625

A14

Intentio


exam

pleproxyservers

0555556

0625

05

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0555556

0625

05

A2Devicea


n

A21Facilityprob

lems

0454545

05

0625

A22Failu

reof

encryptio

nequipm

ent

0555556

04166

670625

A23Unautho

rized

useo

fsecuree

quipment

0555556

0625

0714286

A24Ineffectiv


ent

0454545

0625

0714286

A25Failu

reof

applicationserver

0625

0714286

0625

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0555556

0625

0714286

A32Failu

reof

radioplatform

transm

ission

104166

670714286

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

105

0714286

A34Failu

reof

sensor

networks

0625

05

1A35Failu

reof

potentialofenergy

105

1A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0625

0625

0714286

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0454545

04166

670625

A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0454545

04166

670714286

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0454545

05

0625

A44Failu

reof

real-timed

emandforecasts

0454545

05

05


Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Table11Th

edegreeo

fgreyrelationfore

achfailu

remod

eand

each

dimensio

nandthefi

nalrank

Dim

ensio

nsAs

sociated

activ

ities

Degreeo

fgrey

relatio

n

Degreeo

fgrey

relatio

n(dim

ensio

n)Risk

rank

ing

A1Identifi

catio

nandaccessmanagem

ent

A11L

osso

fsecretk

eys

0613095

0619312

3A12

Cryptanalysisof

acipheredsig

nal

0654762

A13

Secretp

assw

orddivulged

toanyotheru

ser

0708333

A14

Intentio


exam

pleproxyservers

0560185

A15

Spo

ofing

imperson

ationof

alegitimateu

ser

0560185

A2Devicea


n

A21Facilityprob

lems

0526515

0588648

2A22Failu

reof

encryptio

nequipm

ent

0532407

A23Unautho

rized

useo

fsecuree

quipment

0631614

A24Ineffectiv


ent

0597944

A25Failu

reof

applicationserver

0654762

A3Infrastructure

managem

ent

A31Ca

blingprob

lems

0631614

0712743

4

A32Failu

reof

radioplatform

transm

ission

0710317

A33Failu

reof

ciph

eraudio(te

leph

one)andvideo

0738095

A34Failu

reof

sensor

networks

0708333

A35Failu

reof

potentialofenergy

0833333

A36Unautho

rized

readou

tofd

atas

toredon

arem

oteL

AN

0654762

A4Datag

overnance

A41Failu

reof

interpretatio

nandanalysisof

data

0498737

050965

1A42Failu

reof

auditreviewof

implem

entedpo

liciesa

ndinform

ationsecurity

0528499

A43Failu

reto

maxim

izen

ewbu

sinessv

alue

0526515

A44Failu

reof

real-timed

emandforecasts

0484848





D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













D1 0616667 (3)D2 0591629 (2)D3 0645833 (4)D4 0512405 (1)


D1 0621429 (3)D2 0586264 (2)D3 0641071 (4)D4 0507446 (1)


D1 0621528 (3)D2 0589271 (2)D3 0644097 (4)D4 0512216 (1)


D1 061754 (3)D2 058815 (2)D3 064246 (4)D4 0507597 (1)


D1 0619742 (3)D2 0585045 (2)D3 0639633 (4)D4 0504329 (1)


D1 0618968 (3)D2 0591531 (2)D3 0646032 (4)D4 0513907 (1)



Competing Interests


Acknowledgments


References




















































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra









research article a grey theory based approach to...

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