privacy preserving data sharing with anonymous id assignment using aida algorithm
DESCRIPTION
visit http://www.ijcert.org for more research journals it offers discount for Indian Research scholars IJCERT Standard on-line Journal ISSN(Online):2349-7084,(An ISO 9001:2008 Certified Journal) iso nicir csir IJCERT (ISSN 2349–7084 (Online)) is approved by National Science Library (NSL), National Institute of Science Communication And Information Resources (NISCAIR), Council of Scientific and Industrial Research, New Delhi, India.TRANSCRIPT
©IJCERT 23| P a g e
ISSN (Online): 2349-7084
International Journal of Computer Engineering In Research Trends (IJCERT)
Volume 1, Issue 1, July 2014, PP 23-29
www.ijcert.org
Privacy Preserving Data Sharing With Anonymous ID
Assignment Using AIDA Algorithm
Allam.Jyothi1 ,M.Tech Research Scholar,
A.Kusuma2 ,Assistant Professor,
Dr.S.Prem Kumar3, Head of the Department
Department of CSE, G.Pullaiah College of Engineering and Technology
JNTU Anatapur, Andhra Pradesh, India
Abstract: In contemporary world web has created a leeway into daily lives with all information
being hold on during a server of some type so spread or employed in the style required. this can
be confined to multiple applications like patient medical records, balloting details, banking,
social networking, email, analysis etc. however the identity has to be preserved for each the
information and therefore the owner because the case is also that is more and {more} turning
into a drag with additional and more identities allotted to the information particularly just in
case of distributed server sharing. Existing solutions target the central server model that is
computationally costly, includes a immense information measure exchange, information security
is compromised and therefore not fitted to the distributed model hip currently. The proposed
work focuses on the distributed side of computing wherever the IDs are anonymous employing a
distributed computation with no central authority and such IDs will be used as a part of schemes
for sharing or dividing communications information measure, information storage, and
alternative resources anonymously and while not convict. it's doable to use secure add to permit
one to opt-out of a computation beforehand on the premise of sure rules in applied mathematics
revealing limitation. These model suites for the distributed computing model with Anonymous ID
assignment wherever procedure overhead is low, information measure consumption is
additionally less. The AIDA algorithm is applied serially and therefore is secure, however chiefly
Allam.Jyothi,A.Kusuma,Dr.S.Prem Kumar
International Journal of Computer Engineering In Research Trends (IJCERT) 24 | P a g e
the distributed nature of the information sharing system is sustained. The protocol for privacy
conserving mistreatment anonymous id assignment is no-hit.
Keywords: Privacy, Anonymity, Distributed Computing Systems, secure multiparty computation,
Anonymization, Deanonymization, privacy preserving data mining, privacy protection.
I. INTRODUCTION
Large numbers of conclusions in privacy-preserving are obtained by researchers, most of that area unit supported
Associate in Nursing assumption that every party is semi-honest. Web involves and permits sharing of knowledge
however identity of the shared data owner is to be preserved. This is often referred to as maintaining namelessness
that's whistle blower with increasing server storages like cloud this is often a tangle space. specifically, a celebration
is deemed semi-honest once that the party follows the protocol properly with the exception that it keeps a record of
all its intermediate computation results then tries to deduce additional info additionally to the protocol result.
Moreover, researchers additionally assume that each party doesn't conspire or share its record with the other party.
However, reliance on such assumptions proves problematic therein a celebration cannot trust any party while not
taking substantial risk that their protection of their non-public info. Privacy-preserving data processing is regarding
protective the individual privacy and retentive the maximum amount as attainable the data during a dataset to be free
for mining. The perturbation approach and also the k-anonymity model area unit 2 major techniques for this goal.
The k-anonymity model assumes a quasi-identifier (QID) that may be a set of attributes that will function Associate
in nursing symbol within the information set. In the simplest case, it's assumed that the dataset may be a table which
every tuple corresponds to a private. The privacy could also be desecrated if some quasi-identifier values area unit
distinctive within the free table. The idea is that Associate in nursing aggressor will have the data of another table
wherever the quasi-identifier values area unit coupled with the identities of people. Therefore, a be a part of of the
free table with this background table can disclose the sensitive information of people. Think about a situation within
which an outsized range of parties, like several tiny retail stores, collaborate to figure some secure operate of their
inputs. as an example, they require to urge some info relating to market conditions. In such a situation, a number of
the parties concerned could also be enticed to sneak a peek at their competitor’s non-public records. This situation
with four parties, numbered 1, 2, 3 and 4, collaborating to figure some secure operate of their inputs, 1X,2X, 3X and
4X severally. Assume that party one may be a common rival of party a pair of, 3 and 4. Then these parties might
therefore prefer to unite to create a coalition once the execution of the protocol to deduce the privacy info of party
one from the messages received from party one (m2, m3, m4) and also the end result. Strategies for assignment and
victimization sets of pseudonyms are developed for anonymous communication in mobile networks. The strategies
developed in these works typically need a trusty administrator, as written, and their finish merchandise typically
takes issue from ours in kind and/or in applied math properties.
Privacy Preserving Data Sharing With Anonymous ID Assignment Using AIDA Algorithm
International Journal of Computer Engineering In Research Trends (IJCERT) 25 | P a g e
The availability of big numbers of databases recording an outsized sort of info regarding people makes it attainable
to find info regarding specific people by merely correlating all the offered databases. Though confidentiality and
privacy area unit typically used as synonyms, they're totally different concepts: information confidentiality is
regarding the problem (or impossibility) by Associate in nursing unauthorized user to be told something regarding
information hold on within the info. Usually, confidentiality is achieved by implementing Associate in nursing
access policy, and presumably by victimization crypto logic tools. Privacy relates to what information is often safely
disclosed while not unseaworthy sensitive info relating to the legitimate owner. The issues of knowledge
confidentiality and anonymization are thought of singly.
However, a relevant downside arises once information hold on during a confidential, anonymity-preserving info has
to be compelled to be updated. The operation of up-dating such a info, e.g. by inserting a tuple containing info a few
given individual, introduces 2 issues regarding each the namelessness and confidentiality of {the information the
info the information} hold on within the info and also the privacy of the individual to whom the info to be inserted
area unit related: (i) is that the updated info still privacy-preserving? And (ii) will the info base owner have to be
compelled to understand the data to be inserted? Clearly, the 2 issues area unit connected within the sense that they
will be combined into the subsequent problem: can the info owner decide if the updated info still preserves privacy
of people while not directly knowing the new information to be inserted.
II.SYSTEM ARCHITECTURE
This paper builds associate algorithmic rule for sharing straightforward number information on high of secure total.
The sharing algorithmic rule is going to be used at every iteration of the algorithmic rule for anonymous ID
assignment (AIDA). This AIDA algorithmic rule, and also the variants that we have a tendency to discuss, will need
a variable and limitless variety of iterations. Finitely-bounded algorithmic rules for AIDA increase a parameter
within the algorithm can scale back the amount of expected rounds. However, our central algorithmic rule needs
finding a polynomial with coefficients taken from a finite field of integers modulo a main. That task restricts the
amount to which may be much raised.
Fig-1: Data Sharing
Allam.Jyothi,A.Kusuma,Dr.S.Prem Kumar
International Journal of Computer Engineering In Research Trends (IJCERT) 26 | P a g e
III. EXISTING SYSTEM
It permits parties to reckon the ad of their individual inputs while not revealing the inputs to at least one another.
During this system info doesn't maintained anonymously. Anyone will simply access the info. This is the
disadvantage of existing system. Game conjectural approach (LFA) to active distributed data {processing} approach
makes the info mining process as ascendible. This theme isn't attainable for big information bases. Resolution
thought will be generated by Nash equilibrium technique. Secure add calculates the ad of values from the individual
sites. Secure multiparty computation permits the parties to collectively reckon the ad of their individual input while
not revealing the input to a different [3], [13].Secure set union avoids the duplicates throughout the info mining.
During this theme data aren't guarantee that area unit properly correct, assailant will add further data to information
records. Secure size of set intersection gets the common details throughout the info mining. Association rule is new
data discovered at the results of data processing. In EM cluster things will be partitioned off into set of comparable
components. Routing data could be a a part of every packet. By observation the routing data sender and receiver of
the info will be simply known. Onion Routing could be a technique that limits the network vulnerability. It provides
the anonymous socket association over the pc network [12].
This approach secure solely in net service. Homomorphic cryptography is employed to supply security to E-
Gambling. In E-Gambling set of players remotely play a game, for earning cash in order that security are going to be
required. Mental Poker protocol guarantees the fairness of the sport. Homomorphic properties of cryptosystem will
be utilized in Mental Poker protocols [10]. Homomorphic cryptography permits the player to manage the cards co-
operatively. Mental Poker protocols use zero-knowledge proof to confirm the honesty of the sport. In Entity
generated nom de guerre theme entity will generate own pseudonyms. In centralized nom de guerre assignment
admin collects the set of distinctive nom de guerre to avoid repetition of same pseudonyms. In Hybrid nom de
guerre (HP) theme pseudonyms area unit regionally generated and centrally controlled to stop collisions [8].
IV.PROPOSED MODEL
Cloud-based web site management tools offer capabilities for a server to anonymously capture the visitor’s net
actions. The matter of sharing in camera command information in order that the people WHO area unit the themes
of the info can't be known has been researched extensively. Researchers have conjointly investigated the connection
of obscurity and/or privacy in numerous application domains: patient medical records, electronic balloting, e-mail,
social networking, etc.Another style of obscurity, as utilized in secure multiparty computation, permits multiple
parties on a network to collectively do a world computation that depends on information from every party whereas
the info command by every party remains unknown to the opposite parties. A secure computation operate wide
utilized in the literature is secure add that permits parties to reckon the add of their individual inputs while not
revealing the inputs to at least one another.
A. System Design
Privacy Preserving Data Sharing With Anonymous ID Assignment Using AIDA Algorithm
International Journal of Computer Engineering In Research Trends (IJCERT) 27 | P a g e
This system uses economical algorithms for distribution identifiers to the nodes of a network. The IDs area unit
anonymous employing a distributed computation with no central authority in system style Advanced cryptography
customary is employed for each cryptography and coding (AES).
Fig 2 System Architecture
B. Review of Secure add
Suppose that a bunch of hospitals with individual databases want to reason and share solely the common of a
knowledge item, like the amount of hospital no inheritable infections, while not revealing the worth of this
information item for any member of the cluster. Thus, nodes have information things, and need to reason and share
solely the whole worth. A secure add rule permits the add to be collected with some guarantees of obscurity. Again,
assume the semi-honest model of privacy protective data processing. Below this model, each node can follow the
foundations of the protocol, however might use any data it sees throughout the execution of the protocol to
compromise security. Ought to all pairs of nodes have a secure communication out there.
C. Network Setup
In this module the network is setup with a server. Multiple shoppers’ area unit registered and everyone is capable of
information sharing. Thus every shopper could be a peer and thus is actually distributed. Associate degree identity is
assigned however this can be not anonymous. This can be the bottom id. The shopper should register himself and so
transfer the relevant information. The shopper login details area unit hold on within the network information i.e.
SQL Server. This can be referred to as because the account within the cloud server.
Allam.Jyothi,A.Kusuma,Dr.S.Prem Kumar
International Journal of Computer Engineering In Research Trends (IJCERT) 28 | P a g e
D. Data Sharing
Either a shopper requests information or involves submission of information to the appliance sort of a pick or
opinion or a document. of these involve information to be shared by the user. Whereas collating responses from
multiple service suppliers, the master service supplier or broker should open individual sections of the shape. But,
the individual service supplier is meant to open the portion of the shape selected for its own filling up and to not
intervene with anyone else’s space. This needs that the info document be multi-parted and have some suggests that
to shield parts to unwanted service supplier. One level of concern is that the XML content, which can contain data of
multiple heterogeneous service suppliers, might get exposed to at least one service supplier, a node on the grid. This
might have a breach of privacy between multiple service suppliers. Many a times, privacy is closely resembled with
obscurity that demands the requirement of being unidentified or unobserved whereas transacting over property right
like net or different public realm. Adequate level of privacy has to be achieved through controlled speech act of
identity and associated data. Obscurity will guarantee accomplishment of privacy wants. In general, anonymous
message transmission needs that the transacting message wouldn't carry any data regarding the first sender and
supposed receiver. this can be the pre step before the AIDA is applied.
V.AIDA – secure add rule
N nodes want to reason and share solely the common of a knowledge item, while not revealing the worth of this
information item for any member of the cluster. This can be referred to as because the secure add and it's used
slender. Every node chooses random values. Every ―random‖ worth is transmitted from node to node. The add of
these random numbers is desired total. Every node totals all the random values received. Currently every node
merely broadcasts this message to all or any different nodes. All pairs of nodes have a secure communication out
there, a simple, however resource intensive, secure add rule are often created. Suppose that a coalition of the nodes
seeks to garner data regarding the personal information of the opposite nodes. Since the parties area unit semi-
honest, the presumably helpful outside data out there to a coalition seeking to garner the personal data of different
parties, consists of solely the random numbers within the messages received from those parties, the partial sums and
therefore the total. The secure add rule simply given remains N-private even once the input file area unit called a
multiset to all or any parties. we have a tendency to term this input permutation collusion resistance because the
coalition is aware of the info command by the remaining parties solely as a multiset.Decode the printed is exclusive
and thus the rule offers the most security against privacy. The communications necessities of the rules rely heavily
on the underlying implementation of the chosen secure add algorithm. Suppose that our cluster of nodes needs to
share actual information values from their databases instead of wishing on solely applied math data as shown within
the previous section. That is, every member of the cluster of nodes contains a information item that is to be
communicated to all or any the opposite members of the cluster. However, the info is to stay anonymous, collusion
resistant technique for this task mistreatment secure adds as our underlying communication mechanism. Our
information things area unit taken from a, generally finite field. Within the usual case, every are associate degree
whole number worth and can be the field wherever could be a prime satisfying for all. Thus, arithmetic can generally
be performed mistreatment modulus; however different fields will be used.
Privacy Preserving Data Sharing With Anonymous ID Assignment Using AIDA Algorithm
International Journal of Computer Engineering In Research Trends (IJCERT) 29 | P a g e
VI .CONCLUSION
Thus the projected random secure add AIDA rule is foolproof in allocating ID to users and therefore the anonymous
identity is maintained within the user and therefore the information owner isn't compromised below any
circumstances therefore providing ample proof for the sets of users in multiparty environments. Even below tough
things the communications and information measure isn't affected in any manner. Therefore not like cryptanalytic
measures and ancient systems AIDA proves to be secure for distributed design keeping the user safe from prying
persons under fire in several segments. It’s greatly decreases communication overhead. By mistreatment personal
communication that's obscurity router to transmit the info a lot of firmly. To beat the matter of characteristic details
and ever-changing data anonymous id was used. Random serial range is employed to spot whether or not the info
requesting person could be a correct approved person or hackers. The employment of the Newton identities greatly
decreases communication overhead. This could change the employment of a bigger range of ―slots‖ with a resulting
reduction within the range of rounds needed. The answer of a polynomial are often avoided at some expense by
mistreatment Sturm’s theorem. The event of a result almost like the Sturm’s technique over a finite field is an
attractive chance.
REFERENCES:
[1] Sarbanes–Oxley Act of 2002, Title 29, Code of Federal Regulations, Part 1980, 2003.
[2] White Paper—The Essential Guide to Web Analytics Vendor Selection, IBM [Online]. Available:
http://measure.coremetrics.com/corem/getform/reg/wp-evaluation-guide
[3] A. Shamir, ―How to share a secret,‖ Commun. ACM, vol. 22, no. 11, pp. 612–613, 1979.
[4] A. Friedman, R. Wolff, and A. Schuster, ―Providing k-anonymity in data mining,‖ VLDB Journal, vol. 17, no. 4,
pp. 789–804, Jul. 2008.
[5] F. Baiardi, A. Falleni, R. Granchi, F. Martinelli, M. Petrocchi, and A. Vaccarelli, ―Seas, a secure e-voting
protocol: Design and implementation,‖ Comput. Security, vol. 24, no. 8, pp. 642–652, Nov. 2005.
[6] D. Chaum, ―Untraceable electronic mail, return address and digital pseudonyms,‖ Commun. ACM, vol. 24, no.
2, pp. 84–88,Feb. 1981.
[7] Q. Xie and U. Hengartner, ―Privacy-preserving matchmaking for mobile social networking secure against
malicious users,‖ in Proc. 9th Ann. IEEE Conf. Privacy, Security and Trust, Jul. 2011, pp. 252–259.
[8] O. Goldreich, S. Micali, and A. Wigderson, ―How to play any mental game,‖ inProc. 19th Ann. ACM Conf.
Theory of Computing, Jan. 1987, pp. 218–229, ACM Press
[9] A. Yao, ―Protocols for secure computations,‖ in Proc. 23rd Ann. IEEE Symp. Foundations of Computer Science,
1982, pp.160–164, IEEE Computer Society.
[10] C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, ―Tools for privacy preserving distributed data
mining,‖ ACM SIGKDD Expl