a three-layer privacy preserving cloud storage scheme based

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

A

PROJECT REPORT

ON

“A THREE-LAYER PRIVACY PRESERVING CLOUD STORAGE

SCHEME BASED ON COMPUTATIONAL INTELLIGENCE IN FOG

COMPUTING“

Submitted in partial fulfillment for the award of the degree of

BACHELOR OF ENGINEERING

IN

COMPUTER SCIENCE AND ENGINEERING

BY

SHARANYA IYER G S

1NH15CS121

Under the guidance of

Ms. RAJITHA NAIR

Assistant Professor 2,

Dept. of CSE, NHCE

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

CERTIFICATE

It is hereby certified that the project work entitled “A THREE-LAYER PRIVACY

PRESERVING CLOUD STORAGE SCHEME BASED ON COMPUTATIONAL INTELLIGENCE IN

FOG COMPUTING” is a bonafide work carried out by SHARANYA IYER G S (1NH15CS121)

in partial fulfilment for the award of Bachelor of Engineering in COMPUTER SCIENCE

AND ENGINEERING of the New Horizon College of Engineering during the year 2018-

2019. It is certified that all corrections/suggestions indicated for Internal Assessment

have been incorporated in the Report deposited in the departmental library. The project

report has been approved as it satisfies the academic requirements in respect of project

work prescribed for the said Degree.

………………………… ……………………….. ………………………………

Signature of Guide Signature of HOD Signature of Principal

(Ms. Rajitha Nair) (Dr. B. Rajalakshmi) (Dr. Manjunatha)

External Viva

Name of Examiner Signature with date

1. ………………………………………….. ………………………………….

2. …………………………………………… …………………………………..

I

ABSTRACT

Recent years witness the development of cloud computing technology. With the explosive

growth of unstructured data, cloud storage technology gets more attention and better

development. However, in current storage schema, user’s data is totally stored in cloud

servers. In other words, users lose their right of control on data and face privacy leakage

risk. Traditional privacy protection schemes are usually based on encryption technology,

but these kinds of methods cannot effectively resist attack from the inside of cloud server.

In order to solve this problem, we propose a three-layer storage framework based on fog

computing. The proposed framework can both take full advantage of cloud storage and

protect the privacy of data. Besides, Hash-Solomon code algorithm is designed to divide

data into different parts. Then, we can put a small part of data in local machine and fog

server in order to protect the privacy. Moreover, based on computational intelligence, this

algorithm can compute the distribution proportion stored in cloud, fog, and local machine,

respectively. Through the theoretical safety analysis and experimental evaluation, the

feasibility of our scheme has been validated, which is really a powerful supplement to

existing cloud storage scheme.

II

ACKNOWLEDGEMENT

The satisfaction and euphoria that accompany the successful completion of any task

would be impossible without the mention of the people who made it possible, whose

constant guidance and encouragement crowned our efforts with success.

I have great pleasure in expressing my deep sense of gratitude to Dr. Mohan

Manghnani, Chairman of New Horizon Educational Institutions for providing necessary

infrastructure and creating good environment.

I take this opportunity to express my profound gratitude to Dr. Manjunatha , Principal

NHCE, for his constant support and encouragement.

I am grateful to Dr.Prashanth C.S.R, Dean Academics, for his unfailing encouragement

and suggestions, given to me in the course of my project work.

I would also like to thank Dr. B. Rajalakshmi , Professor and Head, Department of

Computer Science and Engineering, for her constant support.

I express my gratitude to Ms. Rajitha Nair , Assistant Professor 2, my project guide, for

constantly monitoring the development of the project and setting up precise deadlines.

Her valuable suggestions were the motivating factors in completing the work.

Finally a note of thanks to the teaching and non-teaching staff of Dept of Computer

Science and Engineering, for their cooperation extended to me, and my friends, who

helped me directly or indirectly in the course of the project work.

SHARANYA IYER G S (1NH15CS121)

III

CONTENTS

ABSTRACT I

ACKNOWLEDGEMENT II

LIST OF FIGURES V

1. INTRODUCTION

1.1. OBJECTIVE 2

1.2. PROJECT STATEMENT 2

2. LITERARY SURVEY

2.1. OBJECTIVES 3

2.2. EXISTING SYSTEM 13

2.3. PROPOSED SYSTEM 14

3. REQUIREMENT ANALYSIS

3.1 FUNCTIONAL REQUIREMENTS 15

3.2 NON FUNCTIONAL REQUIREMENTS 16

3.3 REQUIREMENT SPECIFICATION 18

3.4 TOOLS 19

4 SYSTEM DESIGN ARCHITECTURE

4.1 DESIGN CONSIDERATION 22

4.2 SYSTEM ARCHITECTURE 23

4.3 DESIGN 24

5 IMPLEMENTATION

5.1 DOMAIN SELECTION 31

5.2 PLATFORM SELECTION 31

5.3 GUI DESIGN 33

5.4 MODULES 35

5.5 ALGORITHM 36

IV

6 TESTING

6.1 TYPES 40

6.2 TEST CASES 42

6.3 OUTPUT

7 CONCLUSION 47

REFERENCES 48

V

LIST OF FIGURES

Fig no. Fig.Name Page no.

6.3.1 Home Page 44

6.3.2 Login Page 45

6.3.3 File Upload 45

6.3.4 Hash Files 46

6.3.5 File Division 46

A Three-Layer Privacy Preserving Cloud Storage Scheme

Dept of CSE, NHCE Page 1

CHAPTER 1

INTRODUCTION

Cloud computing has attracted great attention from different sectors of society. Cloud computing

has gradually matured through so many people’s efforts. Then there are some cloud-based

technologies deriving from cloud computing. Cloud storage is an important part of them. With

the rapid development of network bandwidth, the volume of user’s data is rising geometrically.

User’s requirement cannot be satisfied by the capacity of local machine any more. Therefore,

people try to find new methods to store their data. Pursuing more powerful storage capacity, a

growing number of users select cloud storage. Storing data on a public cloud server is a trend in

the future and the cloud storage technology will become widespread in a few years. Cloud

storage is a cloud computing system which provides data storage and management service. With

a cluster of applications, network technology and distributed file system technology, cloud

storage makes a large number of different storage devices work together coordinately. Nowadays

there are a lot of companies providing a variety of cloud storage services, such as Dropbox,

Google Drive, iCloud, Baidu Cloud, etc. These companies provide large capacity of storage and

various services related to other popular applications, which in turn leads to their success in

attracting humorous subscribers. However, cloud storage service still exists a lot of security

problems. The privacy problem is particularly significant among those security issues. In history,

there were some famous cloud storage privacy leakage events. For example, Apples iCloud

leakage event in 2014, numerous Hollywood actresses private photos stored in the clouds were

stolen. This event caused an uproar, which was responsible for the users’ anxiety about the

privacy of their data stored in cloud server. Here, we propose a TLS scheme based on fog

computing model and design a Hash-Solomon code based on Reed-Solomon code. Fog

computing is an extended computing model based on cloud computing which is composed of a

lot of fog nodes. These nodes have a certain storage capacity and processing capability. In our

scheme, we split user’s data into three parts and separately save them in the cloud server, the fog

server and the user’s local machine. Besides, depending on the property of the Hash-Solomon

code, the scheme can ensure the original data cannot be recovered by partial data. On another

hand, using Hash-Solomon code will produce a portion of redundant data blocks which will be

used in decoding procedure. Increasing the number of redundant blocks can increase the



reliability of the storage, but it also results in additional data storage. By reasonable allocation of

the data, our scheme can really protect the privacy of user’ data. The Hash-Solomon code needs

complex calculation, which can be assisted with the Computational Intelligence (CI). Paradigms

of CI have been successfully used in recent years to address various challenges, for example, the

problems in Wireless sensor networks (WSNs) field. CI provides adaptive mechanisms that

exhibit intelligent behavior in complex and dynamic environments like WSNs. Thus in our

paper, we take advantage of CI to do some calculating works in the fog layer. Compared with

traditional methods, our scheme can provide a higher privacy protection from interior, especially

from the CSPs.

1.1 OBJECTIVE

The main objective is to provide the ideal cloud storage and protect the privacy of data by

introducing three-layer storage framework based on fog computing. We used Hash-Solomon

algorithm which designed to divide the data into different parts, later we can put small parts of

data into local machine and fog server in order to protect the privacy.

1.2 PROBLEM STATEMENT

With the exponential growth of unstructured data, the cloud storage gets more attentions since

it’s provided better security and privacy of data but the fact of the matter is the risk of sharing or

leaking the data is also begin to raise in other words user lose their right of control on data and

facing leakage risk. The privacy problem is particularly significant among those security issues.



CHAPTER 2

LITERARY SURVEY

A literature survey or a literature review in a project report shows the various analyses and

research made in the field of interest and the results already published, taking into account the

various parameters of the project and the extent of the project.

A literature survey includes the following

• Existing theories about the topic which are accepted universally.

• Books written on the topic, both generic and specific.

• Research done in the field usually in the order of oldest to latest.

• Challenges being faced and on-going work, if available.

Literature survey describes about the existing work on the given project .It deals with the

problem associated with the existing system and also gives user a clear knowledge on how to

deal with the existing problems and how to provide solution to the existing problems .different

thing.

2.1 OBJECTIVES

• Learning the definitions of the concepts.

• Access to latest approaches, methods and theories.

• Discovering research topics based on the existing research

• Concentrate on your own field of expertise– Even if another field uses the same words, they

usually mean completely

• It improves the quality of the literature survey to exclude sidetracks– Remember to explicate

what is excluded.

2.1.1 Virtual Machine and Bandwidth Allocation in Software Defined Network (SDN) and

Cloud Computing Environments



An optimal algorithm was proposed for provisioning VMs and network bandwidth in a cloud

computing environment, enabled by software defined networking. The algorithm makes a

decision to reserve VMs and network bandwidth from certain cloud providers to minimize costs,

by trading off the cost of over provisioning against the cost of on-demand resources. They have

accounted for uncertain demand by formulating and solving a two-stage stochastic optimization

problem to optimally reserve VMs and bandwidth to minimize cost. They have observed that the

solution is optimal and outperforms alternative methods, achieving results close to those

achieved when demand is perfectly known.

Advantages:

The proposed solution minimizes users’ costs and provides superior performance.

Disadvantages:

Not scalable.

2.1.2 General Systematic Erasure Codes with Optimal Repair Bandwidth and Storage for

Distributed Storage Systems

A family repair-bandwidth-efficient erasure codes was proposed, called the Z codes for

distributed storage systems. The Z codes not only achieve the optimal repair bandwidth for

repairing a single data node’s failure, but also attain the minimum storage property as the RS and

CRS codes. The Z codes have many other beneficial properties that make them suitable for

distributed storage systems. The Z codes have comparable encoding and repairing performances

with the RS codes, and significantly outperform the CRS codes and FMSR codes.

Advantages:

Performance is high.

Efficient & effective.



Disadvantages:

Not flexible.

Doesn't provide robustness.

2.1.3 Computational Intelligence in Wireless Sensor Networks

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense

or monitor physical or environmental conditions cooperatively. WSNs face many challenges,

mainly caused by communication failures, storage and computational constraints and limited

power supply. Paradigms of computational intelligence (CI) have been successfully used in

recent years to address various challenges such as data aggregation and fusion, energy aware

routing, task scheduling, security, optimal deployment and localization. CI provides adaptive

mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs.

CI brings about flexibility, autonomous behavior, and robustness against topology changes,

communication failures and scenario changes. However, WSN developers are usually not or not

completely aware of the potential CI algorithms offer. On the other side, CI researchers are not

familiar with all real problems and subtle requirements of WSNs. This mismatch makes

collaboration and development difficult. This paper intends to close this gap and foster

collaboration by offering a detailed introduction to WSNs and their properties. An extensive

survey of CI applications to various problems in WSNs from various research areas and

publication venues is presented in the paper. Besides, a discussion on advantages and

disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general

evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for

WSNs.

Advantages:

Provides feasibility.

Disadvantages:

Provides high communication costs.



Nodes in most WSNs have limited energy.

2.1.4 Privacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over

Encrypted Outsourced Data

In this paper, the authors proposed a content-aware search scheme, which can make semantic

search more smart. Firstly, they introduce conceptual graphs (CGs) as a knowledge

representation tool. Then, they present our two schemes (PRSCG and PRSCG-TF) based on CGs

according to different scenarios. In order to conduct numerical calculation, they transfer original

CGs into their linear form with some modification and map them to numerical vectors. Second,

they employ the technology of multi-keyword ranked search over encrypted cloud data as the

basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of

privacy-preserving smart semantic search based on CGs. Finally, they choose a real-world data

set: CNN data set to test our scheme. They also analyze the privacy and efficiency of proposed

schemes in detail.

The summarization of the paper is described briefly as follows:

1) The authors used Conceptual Graphs as a knowledge representation to substitute traditional

keywords and solve the problem of privacy-preserving smart semantic search based on

conceptual graphs over encrypted outsourced data. Compared with, it’s more secure and

efficient.

2) They creatively propose a modified linear form of conceptual graphs which makes

quantitative calculation on conceptual graphs possible. In a sense, we facilitate fuzzy retrieval on

conceptual graphs in semantic level.

3) They presented two practical schemes from different aspects to solve the problem of privacy-

preserving smart semantic search based on conceptual graphs over encrypted outsourced data.

They are both secure and efficient, but have their own focus on different aspects.

Advantages:

Secure & efficient.

Provides an effective searchable symmetric encryption scheme.



Disadvantages:

Semantic search over encrypted cloud data is not possible.

2.1.5 A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud

Data

A secure tree-based search scheme over the encrypted cloud data, which supports multi-keyword

ranked search and dynamic operation on the document collection. Specifically, the vector space

model and the widely-used “term frequency (TF) × inverse document frequency (IDF)” model

are combined in the index construction and query generation to provide multi-keyword ranked

search. In order to obtain high search efficiency, the authors constructed a tree-based index

structure and proposed a “Greedy Depth-first Search” algorithm based on this index tree. Due to

the special structure of our tree-based index, the proposed search scheme can flexibly achieve

sub-linear search time and deal with the deletion and insertion of documents. The secure kNN

algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate

relevance score calculation between encrypted index and query vectors.

The contributions are summarized as follows:

1) The authors design a searchable encryption scheme that supports both the accurate multi-

keyword ranked search and flexible dynamic operation on document collection.

2) Due to the special structure of our tree-based index, the search complexity of the proposed

scheme is fundamentally kept to logarithmic. And in practice, the proposed scheme can achieve

higher search efficiency by executing our “Greedy Depth-first Search” algorithm. Moreover,

parallel search can be flexibly performed to further reduce the time cost of search process.

Advantages:

Enable secure, efficient, accurate and dynamic multi-keyword ranked search over

outsourced encrypted cloud data.

Achieve sub-linear search efficiency by exploring a special tree-based index and an

efficient search algorithm.

Disadvantages:



In the proposed scheme, the data owner is responsible for generating updating

information and sending them to the cloud server. Thus, the data owner needs to store the

unencrypted index tree and the information that are necessary to recalculate the IDF

values. Such an active data owner may not be very suitable for the cloud computing

model.

2.1.6 Privacy-Preserving Public Auditing for Secure Cloud Storage

A privacy-preserving public auditing system for data storage security in Cloud Computing. They

utilized the homomorphic linear authenticator and random masking to guarantee that the TPA

would not learn any knowledge about the data content stored on the cloud server during the

efficient auditing process, which not only eliminates the burden of cloud user from the tedious

and possibly expensive auditing task, but also alleviates the users’ fear of their outsourced data

leakage. Considering TPA may concurrently handle multiple audit sessions from different users

for their outsourced data files, they further extend our privacy-preserving public auditing

protocol into a multi-user setting, where the TPA canperform multiple auditing tasks in a batch

manner for better efficiency.

The contribution can be summarized as the following three aspects:

1) The authors motivated the public auditing system of data storage security in Cloud Computing

and provide a privacy-preserving auditing protocol, i.e., our scheme enables an external auditor

to audit user’s outsourced data in the cloud without learning the data content.

2) The proposed scheme is the first to support scalable and efficient public auditing in the Cloud

Computing. Specifically, the scheme achieves batch auditing where multiple delegated auditing

tasks from different users can be performed simultaneously by the TPA.

3) They proved the security and justify the performanceof our proposed schemes through

concrete experiments and comparisons with the state-of-the-art.

Advantages:

Public auditability: to allow TPA to verify the correctness of the cloud data on demand

without retrieving a copy of the whole data or introducing additional online burden to the

cloud users.



Storage correctness: to ensure that there exists no cheating cloud server that can pass the

TPA’s audit without indeed storing users’ data intact.

Privacy-preserving: to ensure that the TPA cannot derive users’ data content from the

information collected during the auditing process.

Batch auditing: to enable TPA with secure and efficient auditing capability to cope with

multiple auditing delegations from possibly large number of different users

simultaneously.

Lightweight: to allow TPA to perform auditing with minimum communication and

computation overhead.

Disadvantages:

Not cost-efficient.

2.1.7 Security and privacy for storage and computation in cloud computing

SecCloud, a privacy-cheating discouragement and secure-computation auditing protocol for data

security in the cloud. They had defined the concepts of un-cheatable cloud computation and

privacy-cheating discouragement and proposed SecCloud to achieve the security goals. To

improve the efficiency, different users’ requests can be concurrently handled through the batch

verification. By the extensive security analysis and performance simulation in our developed

SecHDFS, it is showed that our protocol is effective and efficient for achieving a secure cloud

computing.

The contributions of this paper can be summarized as follows.

Firstly, the authors model the security problems in cloud computing and define the

concepts: uncheatable cloud computation and privacy cheating discouragement in our

cloud computing, which are our design goals.

Secondly, they proposed a basic protocol, SecCloud, to attain data storage security and

computation auditing security as well as privacy cheating discouragement and an

advanced protocol to achieve computation and communication efficiency improvement

through batch verification.



Thirdly, they analyze and prove that SecCloud achieves our design goals and discuss how

to minimize the computation cost by choosing the optimal sampling size.

Finally, they develop a cloud computing experimental environment SecHDFS and

implement SecCloud as a test bed.

Advantages:

Efficient & effective.

Disadvantages:

Doesn't provide privacy and security.

2.1.8 Formulating a Security Layer of Cloud Data Storage Framework Based on Multi

Agent System Architecture

Investigating the problem of data security in cloud data storage, to ensure the correctness of

users’ data in cloud data storage; they proposed a security framework and MAS architecture to

facilitate security of cloud data storage. This security framework consists of two main layers as

agent layer and cloud data storage layer. The propose MAS architecture includes five types of

agents: UIA, UA, DERA, DRA and DDPA. In order to facilitate the huge amount of security,

our MAS architecture offered eleven security attributes generated from four main security

policies of correctness, integrity, confidentially and availability of users’ data in the cloud.

The contributions of the paper are two-fold.

First, this research shall contribute to the new proposed cloud data storage security

framework and would be documented for future use in facilitating cloud data security.

Second, formulate cloud data storage security framework and its architecture to facilitate

the security issues that are offered by MAS of cloud data storage.

Advantages:

Cloud Data Correctness: Ensuring that the cloud data is stored and secured properly.



Cloud Data Confidentially: Ensuring that the cloud data is not disclosed to unauthorized

persons.

Cloud Data Integrity: Ensuring that the cloud data held in a system is a prior

representation of the cloud data and that it has not been modified by an unauthorized

person.

Cloud Data Availability: Ensuring that the cloud data processing resources are not

made unavailable by malicious action.

Disadvantages:

The data stored in the cloud may be frequently updated by the users.

2.1.9 Fog Computing and Its Role in the Internet of Things

Fog Computing is a highly virtualized platform that provides compute, storage, and networking

services between end devices and traditional Cloud Computing Data Centers, typically, but not

exclusively located at the edge of network. The authors had outlined the vision and defined key

characteristics of Fog Computing, a platform to deliver a rich portfolio of new services and

applications at the edge of the network. The motivating examples peppered throughout the

discussion range from conceptual visions to existing point solution prototypes. They envision the

Fog to be a unifying platform, rich enough to deliver this new breed of emerging services and

enable the development of new applications. They welcome collaborations on the substantial

body of work ahead:

1) Architecture of this massive infrastructure of compute, storage, and networking devices

2) Orchestration and resource management of the Fog nodes

3) Innovative services and applications to be supported by the Fog.

Some of the roles of fog computing are being mentioned below:

Edge location, location awareness, and low latency. The origins of the Fog can be traced

to early proposals to support endpoints with rich services at the edge of the network,



including applications with low latency requirements (e.g. gaming, video streaming,

augmented reality).

Geographical distribution. In sharp contrast to the more centralized Cloud, the services

and applications targeted by the Fog demand widely distributed deployments. The Fog,

for instance, will play an active role in delivering high quality streaming to moving

vehicles, through proxies and access points positioned along highways and tracks.

Large-scale sensor networks to monitor the environment, and the Smart Grid are other

examples of inherently distributed systems, requiring distributed computing and storage

resources. Very large number of nodes, as a consequence of the wide geo-distribution, as

evidenced in sensor networks in general, and the Smart Grid in particular.

Support for mobility. It is essential for many Fog applications to communicate directly

with mobile devices, and therefore support mobility techniques, such as the LISP

protocol, that decouple host identity from location identity, and require a distributed

directory system.

Real-time interactions. Important Fog applications involve real-time interactions rather

than batch processing.

Predominance of wireless access.

Heterogeneity. Fog nodes come in different form factors, and will be deployed in a wide

variety of environments.

Interoperability and federation. Seamless support of certain services (streaming is a good

example) requires the cooperation of different providers. Hence, Fog components must

be able to interoperate, and services must be federated across domains.

Support for on-line analytic and interplay with the Cloud. The Fog is positioned to play a

significant role in the ingestion and processing of the data close to the source.

2.1.10 Maximizing real-time streaming services based on a multi servers networking

framework

A novel framework was proposed based on multi servers to collaboratively provide services for

each end user.

This framework exploits the diversity of network and efficiently realize the real time

streaming and backup at the same time.



Based on the proposed framework, they innovatively formulated a brand-new problem in

providing real-time streaming services: Maximum Services(MS).This problem aims to

maximize the total number of streaming flows and/or services.

For the real working applications of real-time streaming services, we innovatively

inserted a two-relay restriction into the framework, and there for simplified the MS

problem.

The authors designed an approximate algorithm with foreseeable performance bounds to

the optimal solution. Extensive simulations were carried out to validate the effectiveness

of the proposed methods for real working applications.

A new metric Possible Loss(PL)was introduced to evaluate the storage and backup

performance. An efficient storage algorithm is designed to achieve the minimized PL.

Advantages:

Provides high-quality services such as real-time streams delivery and storage.

Disadvantages:

Performance is poor.

Not cost-effective.

2.2 EXISTING SYSTEM

Cloud is semi-trusted and propose a framework for urban data sharing by exploiting the attribute-

based cryptography. The scheme they proposed is secure and can resist possible attacks.

Fu et al. propose a content-aware search scheme, which can make semantic search smarter. The

experiments results show that their scheme is efficient.

It is considered that in traditional situation, user’s data is stored through CSP, even if CSP is

trustworthy, attackers can still get user’s data if they control the cloud storage management node.

To avoid this problem, they propose an encrypted index structure based on an asymmetric

challenge-response authentication mechanism.

Feng points out that in paper, the burden of server will increase and data may leak during

transmission in cloud servers. Feng proposes a more concise scheme: encrypting data in closed

cloud environment.



Disadvantages:

Not reliable.

Not completely secured.

Cannot resist internal attacks or prevent the CSP (Cloud Service Provider) from selling

user’s data to earn illegal profit.

The private data will be decoded once malicious attackers get it no matter how advanced

the encryption technologies.

2.3 PROPOSED SYSTEM

A new secure cloud storage scheme in this paper by dividing file with specific code and

combining with three-layer storage scheme based on fog computing model and design a Hash-

Solomon code based on Reed-Solomon code. Fog computing is an extended computing model

based on cloud computing which is composed of a lot of fog nodes. These nodes have a certain

storage capacity and processing capability. In our scheme, we split user’s data into three parts

and separately save them in the cloud server, the fog server and the user’s local machine. The

scheme can ensure the original data cannot be recovered by partial data.

Advantages:

This scheme can protect the image content and image features well from the semi-honest

cloud server.

It’s secure and can resist possible attacks.

It achieves high accuracy, security and efficient.

It enables to protect fine-grained secure of the data.



CHAPTER 3

REQUIREMENT ANALYSIS

System Requirement Specification (SRS) is a central report, which frames the establishment of

the product advancement process. It records the necessities of a framework as well as has a

depiction of its significant highlight. A SRS is essentially an association's seeing (in composing)

of a client or potential customer's frame work necessities and conditions at a specific point in

time (generally) before any genuine configuration or improvement work. It's a two-way

protection approach that guarantees that both the customer and the association comprehend

alternate's necessities from that viewpoint at a given point in time.

The composition of programming necessity detail lessens advancement exertion, as

watchful audit of the report can uncover oversights, mistaken assumptions, and irregularities

ahead of schedule in the improvement cycle when these issues are less demanding to right. The

SRS talks about the item however not the venture that created it, consequently the SRS serves as

a premise for later improvement of the completed item.

The SRS may need to be changed, however it does give an establishment to proceed with

creation assessment. In straightforward words, programming necessity determination is the

beginning stage of the product improvement action. The SRS means deciphering the thoughts in

the brains of the customers – the information, into a formal archive – the yield of the prerequisite

stage. Subsequently the yield of the stage is a situated of formally determined necessities, which

ideally are finished and steady, while the data has none of these properties.

3.1 FUNCTIONAL REQUIREMENTS

Functional Requirement defines a function of software system and how the system must behave

when presented with specific inputs or conditions. These may include calculations, data

manipulation and processing and other specific functionality. The functional requirements of the

project are one of the most important aspects in terms of entire mechanism of modules.

The functional requirements here are:



The user is able to select the file and then encode the data.

After encoding, the data gets saved in the local machine.

Forward the data from the local machine to fog and then store it in the fog server.

Upload the data to the cloud server, allocate the data and save it in the main server.

Key is generated using Hash-Solomon code is so as to provide the encoding

and decoding completely and securely.

Cloud server receives the request and then prepare the data.

Fog server restores the data and then send it to the user.

The user receives the decoded data and then end the process.

3.2 NON-FUNCTIONAL REQUIREMENTS

In addition to the obvious features and functions that you will provide in your system, there are

other requirements that don't actually DO anything, but are important characteristics

nevertheless. These are called "non-functional requirements" or sometimes "Quality Attributes."

For example, attributes such as performance, security, usability, compatibility aren’t a "feature"

of the system, but are a required characteristic. You can't write a specific line of code to

implement them, rather they are "emergent" properties that arise from the entire solution. The

specification needs to describe any such attributes the customer requires. You must decide the

kind of requirements that apply to your project and include those that are appropriate.

Here are some Non-Functional Requirements:

Reliability

The framework ought to be dependable and solid in giving the functionalities. When a

client has rolled out a few improvements, the progressions must be made unmistakable by

the framework. The progressions made by the Programmer ought to be unmistakable both

to the Project pioneer and in addition the Test designer.

Security



Aside from bug following the framework must give important security and must secure

the entire procedure from smashing. As innovation started to develop in quick rate the

security turned into the significant concern of an association. A great many dollars are

put resources into giving security. Bug following conveys the greatest security accessible

at the most noteworthy execution rate conceivable, guaranteeing that unapproved clients

can't get to imperative issue data without consent. Bug following framework issues

diverse validated clients their mystery passwords so there are limited functionalities for

all the clients.

Maintainability

The framework observing and upkeep ought to be basic and target in its approach. There

should not be an excess of occupations running on diverse machines such that it gets hard

to screen whether the employments are running without lapses.

Performance

The framework will be utilized by numerous representatives all the while. Since the

framework will be facilitated on a solitary web server with a solitary database server out

of sight, execution turns into a noteworthy concern. The framework ought not succumb

when numerous clients would be utilizing it all the while. It ought to permit quick

availability to every last bit of its clients. For instance, if two test specialists are all the

while attempting to report the vicinity of a bug, then there ought not to be any irregularity

at the same time.

Portability

The framework should to be effectively versatile to another framework. This is obliged

when the web server, which s facilitating the framework gets adhered because of a few

issues, which requires the framework to be taken to another framework.

Scalability

The framework should be sufficiently adaptable to include new functionalities at a later

stage. There ought to be a typical channel, which can oblige the new functionalities.

Flexibility

Flexibility is the capacity of a framework to adjust to changing situations and

circumstances, and to adapt to changes to business approaches and rules. An adaptable



framework is one that is anything but difficult to reconfigure or adjust because of diverse

client and framework prerequisites. The deliberate division of concerns between the

trough and motor parts helps adaptability as just a little bit of the framework is influenced

when strategies or principles change.

3.3 REQUIREMENT SPECIFICATION

3.3.1 Hardware Requirements:

System : Intel i3 2.1 GHZ

Memory : 4GB.

Hard Disk : 40 GB.

Monitor : 15 VGA Color.

Mouse : Logitech.

3.3.2 Software Requirements:

Operating System : Windows 7 / 8.

Language : JAVA / J2EE

Database : MySQL

Tool : NetBeans, Navicat



3.4 TOOLS

3.4.1 NET BEANS IDE

NetBeans IDE is the official IDE for Java 8. With its editors, code analyzers, and

converters, you can quickly and smoothly upgrade your applications to use new Java 8 language

constructs, such as lambdas, functional operations, and method references. Batch analyzers and

converters are provided to search through multiple applications at the same time, matching

patterns for conversion to new Java 8 language constructs. With its constantly improving Java

Editor, many rich features and an extensive range of tools, templates and samples, NetBeans IDE

sets the standard for developing with cutting edge technologies out of the box. An IDE is much

more than a text editor. The NetBeans Editor indent lines, matches words and brackets, and

highlight source code syntactically and semantically. It also provides code templates, coding tips,

and refactoring tools. The editor supports many languages from Java, C/C++, XML and HTML,

to PHP, Groovy, Javadoc, JavaScript and JSP. Because the editor is extensible, you can plug in

support for many other languages. Keeping a clear overview of large applications, with

thousands of folders and files, and millions of lines of code, is a daunting task. NetBeans IDE

provides different views of your data, from multiple project windows to helpful tools for setting

up your applications and managing them efficiently, letting you drill down into your data quickly

and easily, while giving you versioning tools via Subversion, Mercurial, and Get integration out

of the box. When new developers join your project, they can understand the structure of your

application because your code is well-organized.

Design GUIs for Java SE, HTML5, Java EE, PHP, C/C++, and Java ME applications

quickly and smoothly by using editors and drag-and-drop tools in the IDE. For Java SE

applications, the NetBeans GUI Builder automatically takes care of correct spacing and

alignment, while supporting in-place editing, as well. The GUI builder is so easy to use and

intuitive that it has been used to prototype GUIs live at customer presentations. The cost of

buggy code increases the longer it remains unfixed. NetBeans provide static analysis tools,

especially integration with the widely used FindBugs tool, for identifying and fixing common

problems in Java code. In addition, the NetBeans Debugger lets you place breakpoints in your

source code, add field watches, step through your code, run into methods. The NetBeans Profiler

provides expert assistance for optimizing your application's speed and memory usage, and makes



it easier to build reliable and scalable Java SE, JavaFX and Java EE applications. NetBeans IDE

includes a visual debugger for Java SE applications, letting you debug user interfaces without

looking into source code. Take GUI snapshots of your applications and click on user interface

elements to jump back into the related source code.

Fig.: Snap Shot of Net Beans

3.4.2 MySQL

MySQL ("My Sequel") is (as of 2008) the world's most widely used open source

relational database management system (RDBMS) that runs as a server providing multi-user

access to a number of databases. The SQL phrase stands for Structured Query Language.

The MySQL development project has made its source code available under the terms of

the GNU General Public License, as well as under a variety of proprietary agreements. MySQL

was owned and sponsored by a single for-profit firm, the Swedish company MySQL AB, now

owned by Oracle Corporation.

MySQL is a popular choice of database for use in web applications, and is a central

component of the widely used LAMP open source web application software stack (and other

'AMP' stacks). LAMP is an acronym for "Linux, Apache, MySQL, Perl/PHP/Python." Free-

software-open source projects that require a full-featured database management system often use

MySQL.

MySQL is a relational database management system (RDBMS), and ships with no GUI

tools to administer MySQL databases or manage data contained within the databases. Users may

http://en.wikipedia.org/wiki/Relational_database_management_system

http://en.wikipedia.org/wiki/SQL

http://en.wikipedia.org/wiki/Structured_Query_Language

http://en.wikipedia.org/wiki/Source_code

http://en.wikipedia.org/wiki/GNU_General_Public_License

http://en.wikipedia.org/wiki/Proprietary_software

http://en.wikipedia.org/wiki/Business

http://en.wikipedia.org/wiki/Sweden

http://en.wikipedia.org/wiki/MySQL_AB

http://en.wikipedia.org/wiki/Oracle_Corporation

http://en.wikipedia.org/wiki/LAMP_%28software_bundle%29

http://en.wikipedia.org/wiki/List_of_AMP_packages

http://en.wikipedia.org/wiki/Linux

http://en.wikipedia.org/wiki/Apache_HTTP_Server

http://en.wikipedia.org/wiki/Perl

http://en.wikipedia.org/wiki/PHP

http://en.wikipedia.org/wiki/Python_%28programming_language%29

http://en.wikipedia.org/wiki/Free_software

http://en.wikipedia.org/wiki/Free_software

http://en.wikipedia.org/wiki/Relational_database_management_system

http://en.wikipedia.org/wiki/Graphical_user_interface



use the included command line tools, or use MySQL "front-ends", desktop software and web

applications that create and manage MySQL databases, build database structures, back up data,

inspect status, and work with data records. The official set of MySQL front-end tools, MySQL

Workbench is actively developed by Oracle, and is freely available for use.

MySQL, like most other transactional relational databases, is strongly limited by hard

disk performance. This is especially true in terms of write latency. Given the recent appearance

of very affordable consumer grade SATA interface Solid-state drives that offer zero mechanical

latency, a fivefold speedup over even an eight drive RAID array can be had for a smaller

investment.

3.4.3 NAVICAT PREMIUM

Navicat Premium is a multi-connections database administration tool allowing you to

connect to MySQL, MariaDB, SQL Server, and SQLite, Oracle and PostgreSQL databases

simultaneously within a single application, making database administration to multiple kinds of

database so easy.

Navicat Premium combines the functions of other Navicat members and supports most of

the features in MySQL, MariaDB, SQL Server, SQLite, Oracle and PostgreSQL including Stored

Procedure, Event, Trigger, Function, View, etc.

Navicat Premium enables you to easily and quickly transfer data across various database

systems, or to a plain text file with the designated SQL format and encoding. Also, batch job for

different kind of databases can also be scheduled and run at a specific time. Other features

include Import/ Export Wizard, Query Builder, Report Builder, Data Synchronization, Backup,

Job Scheduler and more. Features in Navicat are sophisticated enough to provide professional

developers for all their specific needs, yet easy to learn for users who are new to database server.

Establish a secure SSH session through SSH Tunnelling in Navicat. You can enjoy a

strong authentication and secure encrypted communications between two hosts. The

authentication method can use a password or public / private key pair. And, Navicat comes with

HTTP Tunnelling while your ISPs do not allow direct connections to their database servers but

allow establishing HTTP connections. HTTP Tunnelling is a method for connecting to a server

that uses the same protocol (http://) and the same port (port 80) as a webserver does.

http://en.wikipedia.org/wiki/Command_line

http://en.wikipedia.org/wiki/MySQL_Workbench

http://en.wikipedia.org/wiki/MySQL_Workbench

http://en.wikipedia.org/wiki/Database_transaction

http://en.wikipedia.org/wiki/Solid-state_drive

http://en.wikipedia.org/wiki/Latency_%28engineering%29#Mechanical_latency

http://en.wikipedia.org/wiki/Latency_%28engineering%29#Mechanical_latency



CHAPTER 4

SYSTEM DESIGN ARCHITECTURE

The one of the important feature of the project is designing the system. The design part provides

the different elements of the system such as, architecture and components. System design solves

the problem by splitting the components of the complex system into smaller components and will

perform and operate on each individual component. The framework configuration prepare

develops general structure building outline. Programming diagram incorporates addressing the

item system works in a shape that might be changed into at least one anticipates. The essential

demonstrated by the end customer must be placed in a systematically way. Diagram is a creative

system; an extraordinary design is the best approach to reasonable structure. The structure

"Layout" is portrayed as "The methodology of applying distinctive frameworks and guidelines

with the ultimate objective of describing a strategy or a system in sufficient purpose important to

permit its physical affirmation". Diverse design segments are taken after to add to the system.

The design detail depicts the segments of the system, the sections or segments of the structure

and their appearance to end-customers.

4.1 DESIGN CONSIDERATION

The explanation behind the plan is to orchestrate the course of action of the issue dictated by the

necessities report. This stage is the underlying stage in moving from issue to the game plan

space. All things considered, start with what is obliged; diagram takes us to work towards how to

satisfy those necessities. The design of the system is perhaps the most essential segment

affecting the way of the item and note worthily affects the later stages, particularly testing and

upkeep. System diagram delineates all the huge data structure, report game plan, yield and

genuine modules in the system and their Specification is picked.

4.1.1 HIGH LEVEL DESIGN

HLD - High Level Design (HLD) is the overall system design - covering the system architecture

and database design. It describes the relation between various modules and functions of the

system data flow, flow charts and data structures are covered under HLD.



High Level Design gives the overall System Design in terms of Functional Architecture details

and Database design. This is very important for the ETL developers to understand the flow of the

system with function and database design wise. In this phase the design team, testers and

customers are plays a major role. Also it should have projects standards, the functional design

documents and the database design document also

A high-level design provides an overview of a solution, platform, system, product, service or

process. Such an overview is important in a multi project development to make sure that each

supporting component design will be compatible with its neighboring designs and with the big

picture.

The highest-level solution design should briefly describe all platforms, systems, products,

services and processes that it depends on and include any important changes that need to be

made to them. In addition, there should be brief consideration of all significant commercial,

legal, environmental, security, safety and technical risks, issues and assumptions.

The idea is to mention every work area briefly, clearly delegating the ownership of more detailed

design activity whilst also encouraging effective collaboration between the various project teams.

Today, most high-level designs require contributions from a number of experts, representing

many distinct professional disciplines. Finally, every type of end-user should be identified in the

high-level design and each contributing design should give due consideration to customer

experience.

4.2 SYSTEM ARCHITECTURE

The structural setup methodology is worried with working up a fundamental\ essential

framework for a system. It incorporates perceiving the genuine parts of the structure and

exchanges between these fragments. The starting design technique of perceiving these

subsystems and working up a structure for subsystem control and correspondence is called

development demonstrating plot and the yield of this framework method is a depiction of the

item basic arranging. The proposed design for this framework is given beneath. It demonstrates

the way this framework is outlined and brief working of the framework.



Fig 4.2 : Three Layer Architecture

4.3 DESIGN

Low-level design (LLD) is a component-level design process that follows a step-by-step

refinement process. This process can be used for designing data structures, required software

architecture, source code and ultimately, performance algorithms. Overall, the data organization

may be defined during requirement analysis and then refined during data design work. Post-

build, each component is specified in detail.

4.3.1 Data Flow Diagram

The DFD is straightforward graphical formalism that can be utilized to speak to a framework as

far as the info information to the framework, different preparing did on this information and the

yield information created by the framework. A DFD model uses an exceptionally predetermined



number of primitive images to speak to the capacities performed by a framework and the

information stream among the capacities.

The principle motivation behind why the DFD method is so famous is most likely in light

of the way that DFD is an exceptionally basic formalism- It is easy to comprehend and

utilization. Beginning with the arrangement of abnormal state works that a framework performs,

a DFD display progressively speaks to different sub capacities. Actually, any various leveled

model is easy to get it.

The human personality is such that it can without much of a stretch see any progressive

model of a framework in light of the fact that in a various leveled model, beginning with an

extremely straightforward and unique model of framework, distinctive points of interest of a

framework are gradually presented through the diverse orders. A data-flow diagram (DFD) is a

graphical representation of the "stream" of information through a data framework. DFDs can

likewise be utilized for the perception of information handling.



DATA FLOW DIAGRAM OF STORE PROCEDURE

Start

User select

the file

Encode the

data

Store in the

local machine

Upload the

data to fog

Encode the

information

Select the

server

Allocate the

data

Get the relevant

information

Storage Main

server

End the process



DATA FLOW DIAGRAM FOR DOWNLOAD PROCEDURE

Upload the

data to cloud

Store in fog

server

Cloud receives

request

Fog server

receives the data

Data from

cloud

Decoding

From local

machine

User receives

the data

Data

End the process

Prepare

data

Restore

the data



4.3.2 USE CASE DIAGRAM

A use case chart is a kind of behavioral graph made from a Use-case examination. Its

object is to present a graphical diagram of the usefulness gave by a framework regarding

performers, their objectives (spoke to as utilization cases), and any conditions between those

utilization cases. Use case chart gives us the data about how that clients and utilization cases are

connected with the framework. Use cases are used amid prerequisites elicitation and examination

to speak to the usefulness of the framework. Use cases concentrate on the conduct of the

framework from an outside perspective.



4.3.3 ACTIVITY DIAGRAM

Start

Select

the file

Upload the data

to fog server

Upload the

data to cloud

Save to cloud

storage

End

Allocate

the data

Prepare

the data



CHAPTER 5

IMPLEMENTATION

The implementation phase involves the actual materialization of the ideas, which are

expressed in the analysis document and developed in the design phase. Implementation should be

perfect mapping of the design document in a suitable programming language in order to achieve

the necessary final product. Often the product is ruined due to incorrect programming language

chosen for implementation or unsuitable method of programming. It is better for the coding

phase to be directly linked to the design phase in the sense if the design is in terms of object

oriented terms then implementation should be preferably carried out in a object oriented way.

The implementation involves:

1. Careful planning.

2. Investigation of the current system and the constraints on implementation.

3. Training of staff in the newly developed system.

Implementation of any software is always preceded by important decisions regarding

selection of the platform, the language used, etc. these decisions are often influenced by several

factors such as real environment in which the system works, the speed that is required, the

security concerns, and other implementation specific details. There are three major

implementation decisions that have been made before the implementation of this project. They

are as follows:

1. Selection of the platform (Operating System).

2. Selection of the programming language for development of the application.

3. Coding guideline to be followed.

The GUI is developed using Android Studio, which is the client side. Android Studio is

the official integrated development environment (IDE) for the Android platform. The server side

of the implementation is done using NetBeans. NetBeans is a software development platform



written in Java. The NetBeans Platform allows applications to be developed from a set of

modular software components called modules. Applications based on the NetBeans Platform,

including the NetBeans integrated development environment (IDE), can be extended by third

party developers. The NetBeans IDE is primarily intended for development in Java, but also

supports other languages, in particular PHP, C/C++ and HTML5.

The protocol implemented in this project is Light Weight Anonymous Authentication

Protocol. This protocol aims at providing Data Freshness, Authentication, Secure Localization

and Maintains Anonymity.

5.1 DOMAIN SELECTION

Java is a little, basic, safe, item situated, translated or rapidly improved, byte coded,

engineering, waste gathered, multithreaded programming dialect with a specifically exemption

taking care of for composing circulated and powerfully extensible projects.

With most programming dialects, you either accumulate or translate a project so you can run it

on your PC. The Java programming dialect is irregular in that a project is both accumulated and

deciphered. The stage autonomous codes deciphered by the mediator on the Java stage. The

mediator parses and runs every Java byte code guideline on the PC. Aggregation happens just

once; understanding happens every time the project is executed. The accompanying figure

delineates how this function You can consider Java byte codes as the machine code directions for

the Java Virtual Machine (Java VM). Each Java mediator, whether it’s an advancement device or

a Web program that can run applets, is an execution of the Java VM.

5.2 PLATFORM SELECTION

A platform is the hardware or software environment in which a program runs. As already

mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS.

Most platforms can be described as a combination of the operating system and hardware. The

Java platform differs from most other platforms in that it’s a software-only platform that runs on

top of other hardware-based platforms.

The Java platform has two components:

• The Java Virtual Machine (JVM)

• The Java Application Programming Interface (Java API)



We’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported

onto various hardware-based platforms.

The Java API is a large collection of ready-made software components that provide many useful

capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into

libraries of related classes and interfaces; these libraries are known as packages.

The figure shown below depicts a program that’s running on the Java platform. As the figure

shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware

platform. As a platform-independent environment, the Java platform can be a bit slower than

native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code

compilers can bring performance close.

5.2.1 Input Design

The info outline is the connection between the client and data framework and creating particular

and strategies for information arrangement and those strides are important to put exchange

information into a usable structure for handling can be accomplished by investigating the PC to

peruse information from a composed or printed archive or it can happen by having individuals

entering the information specifically into the framework.

5.2.2 Output Design

A quality output is one, which meets the necessities of the end client and presents the data

obviously. In any framework consequences of preparing are conveyed to the clients and to other

framework through yields. In yield outline it is resolved how the data is to be dislodged for

prompt need furthermore the printed version yield. It is the most essential and direct source data

to the client. Productive and insightful yield outline enhances the framework's relationship to

help client choice making.



5.3 GRAPHICAL USER INTERFACE DESIGN

Graphical user interface is a type of user interface that allows users to interact with

electronic devices using images rather than text commands, A GUI represents the information &

action available to a user through graphical icons & visual indicators such as secondary notation,

as opposed to text-based interfaces, type command labels or text navigation. The actions are

usually performed through direct manipulation of the graphical elements. A GUI uses a

combination of technologies & devices to provide a platform that the user can interact with, for

the tasks of gathering & producing information.

A series of elements confirming a visual language have evolved to represent information

stored in computers. This makes it easier for people with a few computer skills to work with and

use computer software.

Designing the visual composition & temporal behavior of a GUI is an important part of

software application programming in the area of human-computer interaction. Its goal is to

enhance the efficiency & ease of use for the underlying logical design of a stored program, a

design discipline known as usability. Methods of user-centered design are used to ensure that the

visual language introduced in the design is well tailored to the tasks.

5.3.1 OVERVIEW

Java was conceived by James Gosling, Patrick Naughton, Chris wrath, Ed Frank, and

Mike Sheridan at Sun Micro system. It is a platform independent programming language that

extends its features wide over the network. Java2 version introduces a new components than are

possible with AWT

• It’s a light weight package, as they are not implemented by platform-specific code.

• Related classes are contained in javax.swing and its sub packages, such as

javax.swing.tree

• Components explained in the swing have more capabilities than those of AWT.



The Java programming language is a high-level language that can be characterized by all of

the following buzzwords

• Simple

• Architecture neutral

• Object oriented

• Portable

• Distributed

• High performance

• Interpreted

• Multithreaded

• Robust

• Dynamic

• Secure

With most programming languages, it either compile or interpret a program so that it can

run it on our computer. The Java programming language is unusual in that a program is both

compiled and interpreted. With the compiler, first it translate a program into an intermediate

language called Java byte codes.

Java byte codes are the platform-independent codes interpreted by the interpreter on the

Java platform. The interpreter parses and runs each Java byte code instruction on the computer.

Compilation happens just once; interpretation occurs each time the program is executed. The

following figure illustrates how this works.

We can think of Java byte codes as the machine code instructions for the Java Virtual

Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser

that can run applets, is an implementation of the Java VM. Java byte codes help make “write

once, run anywhere” possible. You can compile your program into byte codes on any platform

that has a Java compiler. The byte codes can then be run on any implementation of the Java VM.

That means that as long as a computer has a Java VM, the same program written in the Java

programming language can run on Windows 2000, a Solaris workstation, or on an iMac.



5.4 MODULES:

5.4.1 Fog Computing

Our scheme is based on fog computing model, which an extension of cloud is computing.

Compared to highly concentrated cloud computing, fog computing is closer to edge network and

has many ad vantages as follows: broader geographical distributions, higher real-time and low

latency. In considering of these characters, fog computing is more suitable to the applications

which are sensitive to delay. Fog computing is usually a three-level architecture, the up-most is

cloud computing layer which has powerful storage capacity and compute capability. The next

level is fog computing layer. The fog computing layer serves as the middle layer of the fog

computing model and plays a crucial role in transmission between cloud computing layer and

sensor network layer. The fog nodes in fog computing layer has a certain storage capacity and

compute capability. The bottom is wireless sensor network layer. The main work of this layer is

collecting data and uploading it to the fog server. Besides, the transfer rate between fog

computing layer and other layers is faster than the rate directly between cloud layer and the

bottom layer. The introduction of fog computing can relief the cloud computing layer, improving

the work efficiency. In our scheme, we take advantage of the fog computing model, adopt three-

layer structure. Furthermore, we replace the WSNs layer by user’s local machine.

5.4.2 Three-Layer Privacy Preserving Cloud Storage Scheme Based on Fog

Computing Model

In order to protect user’s privacy, we propose a TLS frame-work based on fog computing

model. The TSL framework can give user a certain power of management and effectively protect

user’s privacy. As mentioned, the interior attack is difficult to resist. Traditional approaches

work well in solving outside at-tack, but when CSP itself has problems, traditional ways are all

invalid. Different from the traditional approaches, in our scheme, user’s data is divided into three

different-size parts with encoding technology. Each of them will lack a part of key information

for confidentiality. Combining with the fog computing model, the three parts of data will be

stored in the cloud server, the fog server and user’s local machine according to the order from

large to small. By this method, the attacker cannot recover the user’s original data even if he gets



all the data from a certain server. As for the CSP, they also cannot get any useful information

without the data stored in the fog server and local machine because both of the fog server and

local machine are controlled by users.

5.4.3 Implementation Detail of Workflow

i. Stored Procedure: When user wants to store his file to thecloud server, first of all, user’s file

will be encoded with Hash-Solomon code. And then, the file will be divided into several data

blocks and the system will also feedback encoding information simultaneously. Secondly, after

receiving the 99% data blocks from user’s machine, these data blocks will be encoded with

Hash-Solomon again. These data blocks will be divided into smaller data blocks and generates

new encoding information. Thirdly, after cloud server received the data blocks form fog side,

these data blocks will be distributed by cloud manage system. Finally, the storage procedure ends

when all the related information be recorded in different servers.

ii. Download Procedure: When user wants to download his file from the cloud server, firstly,

cloud server receives user’s request and then integrates the data in different distributed servers.

Secondly, the fog server receives the data from the cloud server. Then the fog server returns the

99% data to the user. Thirdly, the user receives the data from fog server. User can get the

complete data by repeating the above steps.

5.5 ALGORITHM

5.5.1 Hash-Solomon code algorithm:

Based on the Reed-Solomon code algorithm, we propose a Hash-Solomon code algorithm. The

Hash-Solomon encoding process is actually a matrix operation. As shown in the diagram below,

firstly we should do mapping transformation on the file which is prepared to be stored, so that

each word of the file corresponds to a number in GF(2ω ). After mapping transformation we get

file matrix O. Secondly we do hash transform on matrix O and get matrix X. Then we multiply

the transformed matrix X by the encoding matrix A. The multiplication will generate k data

blocks X1 to X6 and m redundant data blocks C (k = 6, m= 1). In the same figure below, we



prepare to save X1 to X5 in the Cloud and Fog, and store X6 and C in the local machine. The next

step is similar to the above operations, we do hash transform on X' and get file matrix Y. Then we

multiply the transformed matrix Y by the encoding matrix B. At last, we store Y1 to Y4 in the

cloud server and store Y5 and R in the fog server (k = 5, m = 1). The encoding matrix usually

consists of an identity matrix and a Vandermonde matrix or a Cauchy matrix.

Fig. Diagram of download procedure.

Hash-Solomon code has the following properties: in the k+m data blocks, if we have at least k

data blocks, we can recover the original data combining with the encoding matrix. But once the

number of data blocks is less than k, it cannot be recovered. Using the above properties in our

scheme, after each encoding, we store less than k parts of data blocks in the higher server and

store the remainder parts of data blocks in the lower server. With such reasonable allocation, the

cloud server, the fog server and user’s local machine independently store a certain percentage of

data blocks. It is impossible to recover the original data with any single server’s data. The TLS

framework largely solves the leakage of user’s privacy.



5.5.2 Feasibility study

The feasibility of the project is analyzed in this phase and business proposal is put forth with

a very general plan for the project and some cost estimates. During system analysis the

feasibility study of the proposed system is to be carried out. This is to ensure that the

proposed system is not a burden to the company. For feasibility analysis, some

understanding of the major requirements for the system is essential.

5.5.3 Economical feasibility

This study is carried out to check the economic impact that the system will have on the

organization. The amount of fund that the company can pour into the research and development

of the system is limited. The expenditures must be justified. Thus the developed system as well

within the budget and this was achieved because most of the technologies used are freely

available. Only the customized products had to be purchased.

5.5.4 Technical feasibility

This study is carried out to check the technical feasibility, that is, the technical requirements of

the system. Any system developed must not have a high demand on the available technical

resources. This will lead to high demands on the available technical resources. This will lead to

high demands being placed on the client. The developed system must have a modest

requirement, as only minimal or null changes are required for implementing this system.

5.5.5 Social feasibility

The aspect of study is to check the level of acceptance of the system by the user. This

includes the process of training the user to use the system efficiently. The user must not feel

threatened by the system, instead must accept it as a necessity. The level of acceptance by the

users solely depends on the methods that are employed to educate the user about the system and

to make him familiar with it. His level of confidence must be raised so that he is also able to

make some constructive criticism, which is welcomed, as he is the final user of the system.



CHAPTER 6

TESTING

Testing is an important phase in the development life cycle of the product. This is the

phase, where the remaining errors, if any, from all the phases are detected. Hence testing

performs a very critical role for quality assurance and ensuring the reliability of the software.

During the testing, the program to be tested was executed with a set of test cases and the

output of the program for the test cases was evaluated to determine whether the program was

performing as expected. Errors were found and corrected by using the below stated testing steps

and correction was recorded for future references. Thus, a series of testing was performed on the

system, before it was ready for implementation.

It is the process used to help identify the correctness, completeness, security, and quality

of developed computer software. Testing is a process of technical investigation, performed on

behalf of stake holders, i.e. intended to reveal the quality-related information about the product

with respect to context in which it is intended to operate. This includes, but is not limited to, the

process of executing a program or application with the intent of finding errors.

The quality is not an absolute; it is value to some person. With that in mind, testing can

never completely establish the correctness of arbitrary computer software; Testing furnishes a

‘criticism’ or comparison that compares the state and behaviour of the product against

specification. An important point is that software testing should be distinguished from the

separate discipline of Software Quality Assurance (SQA), which encompasses all business

process areas, not just testing.

There are many approaches to software testing, but effective testing of complex products

is essentially a process of investigation not merely a matter of creating and following routine

procedure.

Although most of the intellectual processes of testing are nearly identical to that of

review or inspection, the word testing is connoted to mean the dynamic analysis of the product-

putting the product through its paces. Some of the common quality attributes include capability,

reliability, efficiency, portability, maintainability, compatibility and usability.



A good test is sometimes described as one, which reveals an error; however, more recent

thinking suggest that a good test is one which reveals information of interest to someone who

matters within the project community.

6.1 TYPES

6.1.1 Unit Testing

Individual component are tested to ensure that they operate correctly. Each component is

tested independently, without other system component. This system was tested with the set of

proper test data for each module and the results were checked with the expected output. Unit

testing focuses on verification effort on the smallest unit of the software design module. This is

also known as MODULE TESTING. This testing is carried out during phases, each module is

found to be working satisfactory as regards to the expected output from the module.

6.1.2 Integration Testing

Integration testing is another aspect of testing that is generally done in order to uncover errors

associated with flow of data across interfaces. The unit-tested modules are grouped together and

tested in small segment, which make it easier to isolate and correct errors. This approach is

continued unit I have integrated all modules to form the system as a whole.

6.1.3 System Testing

System testing is actually a series of different tests whose primary purpose is to fully exercise

the computer-based system. System testing ensures that the entire integrated software system

meets requirements. It tests a configuration to ensure known and predictable results. An example

of system testing is the configuration oriented system integration testing. System testing is based

on process description and flows, emphasizing pre-driver process and integration points

6.1.4 Performance Testing

The performance testing ensure that the output being produced within the time limits and time

taken for the system compiling, giving response to the users and request being send to the

system in order to retrieve the results.



6.1.5 Validation Testing

The validation testing can be defined in many ways, but a simple definition is that. Validation

succeeds when the software functions in a manner that can be reasonably expected by the end

user.

6.1.6 Black Box testing

Black box testing is done to find the following

• Incorrect or missing functions

• Interface errors

• Errors on external database access

• Performance error

• Initialization and termination error

6.1.7 White Box Testing

This allows the tests to

• Check whether all independent paths within a module have been exercised at least

once

• Exercise all logical decisions on their false sides

• Execute all loops and their boundaries and within their boundaries

• Exercise the internal data structure to ensure their validity

• Ensure whether all possible validity checks and validity lookups have been

provided to validate data entry.

6.1.8 Acceptance Testing

This is the final stage of testing process before the system is accepted for operational use.

The system is tested within the data supplied from the system procurer rather than simulated data



6.2 TEST CASES

Sl # 3Test 3Case UTC-*1

Name3of3Test Saving data to the local machine

Expected3Result

The 1% data gets stored into local machine before uploading to

the cloud.

Actual3output Same3as3expected.

Remarks3 Successful

S2# 3Test 3Case UTC-*2

Name3of3Test Fog servers

Expected3Result After being forwarded the data from local machine it should

stores 5% of data and remaining should forward for cloud

storage.


Remarks3 Successful


Name3of3Test Cloud Servers

Expected3Result Eventually the remaining data gets stored into the cloud

server and 100% uploaded.

Actual3output 3Same3as3expected.



Remarks3 3Successful


Name3of3Test Hash-Solomon code

Expected3Result The key should get passed with this code so as to provide

the encoding and decoding completely and securely


Remarks3 Successful


Name3of3Test Divide into chunks.

Expected3Result Divide the files into small chunks and save it to the sever

in different potions.


Remarks3 Successful


Name3of3Test View the file.

Expected3Result The user should be able to view the files that are saved in

the server.






Name3of3Test Download the file.

Expected3Result The files gets merged into a single unit and the user

should be able to download the merged file.



6.3 OUTPUT

6.3.1 HOME PAGE



6.3.2 LOGIN PAGE

6.3.3 FILE UPLOAD



6.3.4 HASH FILES

6.3.5 FILE DIVISION



CHAPTER 7

CONCLUSION

The development of cloud computing brings us a lot of benefits. Cloud storage is a convenient

technology which helps users to expand their storage capacity. However, cloud storage also

causes a series of secure problems. When using cloud storage, users do not actually control the

physical storage of their data and it results in the separation of ownership and management of

data. In order to solve the problem of privacy protection in cloud storage, we propose a TLS

framework based on fog computing model and design a Hash-Solomon algorithm. in order to

achieve the maximum efficiency, and we also find that the Cauchy matrix is more efficient in

coding process.

In future, the theoretical safety analysis, the scheme is proved to be feasible. By allocating the

ratio of data blocks stored in different servers reasonably, we can ensure the privacy of data in

each server the experiment test, this scheme can efficiently complete encoding and decoding

without influence of the cloud storage efficiency.



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a three-layer privacy preserving cloud storage scheme based

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