Cloud Data Security for Usersijarcsms.com/docs/paper/volume4/issue6/V4I6-0099.pdf · infrastructure. So, it is more prone to security threats and vulnerabilities. At present, a major

© 2016, IJARCSMS All Rights Reserved 223 | P a g e

ISSN: 2321-7782 (Online) Impact Factor: 6.047

Volume 4, Issue 6, June 2016

International Journal of Advance Research in Computer Science and Management Studies

Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com

Cloud Data Security for Users Sujit Laxman Thorat

1

Department of Computer Engineering,

G. S. Moze College of Engineering,

Savitribai Phule Pune University,

Balewadi, Pune-411045, India

Pradnya R. Patange2

Department of Computer Engineering,

G. S. Moze College of Engineering,

Savitribai Phule Pune University,

Balewadi, Pune-411045, India

Abstract: Many users use cloud for storage and processing of data for maintaining Backup of Data. Here, the question is

that should users trust the security services provided by cloud as it is managed by third party. Previous System’s security

provided for users data consists of digital watermarking for image, secret sharing of data(image). But secret shares of image

data are not protected in existing system and are vulnerable to attackers. Watermark in existing system works only for

images and not for video/audio. In existing system, Reed-Solomon code is used for correction of transmission errors. The

required processing power for R-S code increases quadratically and as the required maximum level of protection increases,

the additional overhead needed for the R-S code increases with very large amount. Proposed System consists of advanced

watermarking operations so that all multimedia data such as Image, Video and Audio can be watermarked for

authentication of user data. Multimedia Data is splitted into shares in such a way that data remains secure. Also, AES

encryption protection is provided for each share of multimedia so that data is kept protected from attackers. DF Raptor code

is used for transmission error correction which has much better results than R-S code. Additional overhead for required

maximum level of protection is much lesser in case of DF Raptor code than that of R-S code.

Keywords: AES, encryption, DF Raptor, watermarking, authentication, transmission error, Additional overhead.

I. INTRODUCTION

With the fast development of current information technology, electronic publishing, such as the distribution of digitized

images and videos is becoming more and more popular. Digital multimedia content such as images and videos can easily be sent

through the Internet to the cloud system. In particular, data access over wireless networks from the media cloud has recently

found increased popularity due to the fast growth of wireless multimedia applications. However, multimedia security has

become an increasingly major concern for cloud media data access control. It is important to ensure secure and reliable

multimedia data transmissions between users and the media cloud. Since the data can be transferred and stored in a cloud

system through wireless, it becomes vulnerable to unauthorized disclosures, modifications, and replay attacks. A critical

question must be answered when the mobile clients upload their multimedia to the cloud: can users trust the media cloud?

Cloud computing supports distributed service oriented architecture, multi-user and multi-domain administrative

infrastructure. So, it is more prone to security threats and vulnerabilities. At present, a major concern in cloud adoption is

towards its security and privacy. Security and privacy issues are of great concern to cloud service providers who are actually

hosting the services. In most cases, the provider must guarantee that their infrastructure is secure and clients' data and

applications are safe, by implementing security policies and mechanisms. The security issues are organized into several general

categories: trust, identity management, software isolation, data protection, availability reliability, ownership, data backup, data

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Sujit et al., International Journal of Advance Research in Computer Science and Management Studies

Volume 4, Issue 6, June 2016 pg. 223-231

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portability and conversion, multi-platform support and intellectual property. In this paper, some of the techniques are discussed

that were implemented to protect data and propose architecture to protect data in cloud.

II. LITERATURE SURVEY

A. Existing System

Watermarking provides a possible solution to ensure and safeguard copyright and intellectual property rights for online

multimedia content. Multimedia has its own characteristics, and the traditional security methods for the mobile cloud have

limitations such as higher computational and communication overhead. On the other hand, many researchers are aware of the

issues of copyright protection, image authentication, proof of ownership, and so on. Existing System provides Digital

Watermarking authentication security and Secret Sharing of Data in multiple clouds. Discrete wavelets transform (DWT)-based

watermarking is within the category of frequency domain watermarking techniques. The process of transforming the image into

its transform domain varies; hence, the resulting coefficients are different. Generally, the watermarked data are embedded in a

transformed image. In other words, the watermark is inserted into transformed coefficients of the input image. Finally, inverse

transform is performed on the watermarked image. The watermark detection process is the inverse procedure of the watermark

insertion process.

Furthermore, existing system also proposed a secret sharing scheme to divide multimedia data into multiple pieces and then

uploading them to different cloud servers. In this situation, even the data pieces in one cloud are disclosed, but all of the

information cannot be disclosed due to the nature of secret sharing. The secret image sharing scheme is widely applied in visual

cryptography. To secure an image, a secret image sharing scheme usually divides the image into n unreadable images (shares).

The secret image can be recovered by at least a threshold number (r) of shares known as the (r, n) secret sharing threshold.

Also, existing system provides Reed-Solomon Code for transmission error corrections in data which is good in extracting data

bits than general extraction algorithm. Watermark can be attacked by transmission errors and it may become undetectable due to

errors. Watermark errors can be corrected using RS code which is better than other codes for watermark.

Limitations of Existing System

Secret shares of data in existing system are not protected by security mechanism such as Encryption. If Secret Shares are

passed in sequence over a network then Secret Image can be obtained easily by combining shares. Data privacy approach is not

provided in existing system. No security provided for image shares in existing system. This is main limitation of existing

system. Watermark algorithm in existing system works only for images and not for video/audio. As existing system uses

Wavelet Coefficient and three level DWT technique for Watermarking, it does not work for audio/video. The required

processing power for R-S code increases quadratically and as the required maximum level of protection increases, the additional

overhead needed for the R-S code increases with very large amount.

III. PROPOSED SYSTEM

Proposed System consists of following algorithms:

A. Watermark Embedding and Extraction algorithms:

Watermarking is the process of hiding digital information in a carrier signal. Digital watermarks may be used to verify the

authenticity or integrity of the carrier signal or to show the identity of its owners. It is prominently used for tracing copyright

infringements and for banknote authentication. Image such as jpg, png, bmp etc. can be used as Watermark image to embed into

multimedia data. For embedding watermark, Password of user is used as key for embedding watermark into input file.

Watermark is mainly used for authentication or copyright of user over data stored on cloud. For each different format of




multimedia data, a byte offset is set in WatermarkOperations class for embedding watermark into Master file from that byte

offset address.

Open Master and Watermark files in Input Mode.

Open Target(Composite) file in Output Mode.

If compression != 0 then features= CEF else features= UEF.

Calculate watermark size, master file size and target file size.

Get Master byte array, Watermark byte array and initialize target byte array.

Check master file extension against image, video and audio formats.

Set Offset address of file format into inputoutputmarker.

Obtain master data characteristics.

Embed bytes of Watermark into target byte array using inputoutputmarker and secret key.

Write 1 byte for features into target array.

Embed the 3 byte version information into the target byte array.

Embed 1 byte compression ratio into the target byte array.

Embed Master file bytes into target byte array using inputoutputmarker, watermark bytes size, file format and master file

data characteristics.

Arrange target byte array in such a way that watermark is hided inside master file data.

Watermark Extraction algorithm is reverse of Watermark Embedding algorithm.

B. File Splitting and Merging Algorithm:

After watermark embedding into multimedia file, file is split into 4 shares. Splitting is in such a way that data is not easily

identified by an attacker after splitting. This technique removes drawback of Secret Shares generation technique used in existing

system. Split shares are then passed to next step in flow.

Input : F, N.

Output: Sending ‘fc’ to destination cloud.

Step 1: Find File Size in bytes.

F_Sz = Sizeof (FN).

Step 2: Make ‘n’ Chunks of file in such a way that data is not easily identified by an attacker after splitting.

Where fc_Sz = F_Sz/N.

FC = {fc1, fc2,………, fcn}

Step 3: Send each chunk to the destined cloudserver using access-token.

For j=1 to N do

Get access-token[j] from FTP setting module;




fc[j]access-token[j]; //send each file chunk to specified Cloud Server.

Merging algorithm is reverse of Splitting algorithm.

C. AES Encryption algorithm:

Fig. 1 AES Encryption Algorithm

It is based on ‘substitution–permutation network’.

Three types of keys can be used such as 128-bit, 192-bit or 256-bit key.

It is symmetric block cipher algorithm.

Performs computations on bytes rather than bits.

For different Key Size, different number of rounds are executed for input block of data.

Input data is divided into blocks of 128 bits(16 bytes) each.

Each split share of watermarked multimedia is encrypted using AES Encryption technique. This gives protection to each

share of data from attacker where as in existing system, there is no such protection given.

D. DF Raptor Code algorithm:

DF Raptor is an erasure correction code, capable of correcting “missing” or “lost” data. By recovering lost data packets

without requiring retransmission from the sender, DF Raptor efficiently and effectively provides reliability in data networks.

DF Raptor code offers:

The ability to generate a potentially limitless amount of encoded data from any original set of source data, thereby

supporting extremes of low or high network loss as needed for a particular application.

The ability to recover the original data from an amount of successfully received encoded data only slightly greater than the

original source data, regardless of which specific encoded data has been received.

Exceptionally fast encoding and decoding algorithms - operating at nearly symmetric speeds that grow only linearly with

the amount of source data to be processed and independently of the actual amount of loss – so that DF Raptor is highly

scalable and suitable for lightweight software-based solutions.

DF Raptor guarantees error-free delivery in end-to-end communications over any data network, enhancing the quality of

streaming content or accelerating the delivery of data.




Fig. 2 DF Raptor code process

IV. SYSTEM ARCHITECTURE

Fig. 3 System Architecture

V. MATHEMATICAL MODEL

Design of the watermark signal to be added to the host signal. Typically, the watermark signal depends on a key and

watermark information

Possibly, it may also depend on the host data into which it is embedded.

(2)

Design of the embedding method itself that incorpo-rates the watermark signal into the host data yielding

watermarked data

(1)




(3)

Design of the corresponding extraction method that recovers the watermark information from the signal mixture using the

key and with help of the original

(4)

A DF Raptor code with k input symbols that produces n output symbols using symbols of size S bytes is designated Raptor

(n,k,S). Raptor Codes can produce a potentially infinite stream of symbols such that any subset of symbols of size k (1+ε) is

sufficient to recover the original k symbols with high probability. The original symbols are recovered from the collected ones

with O (k log (1/ε)) operations and output symbol is generated using O (log (1/ε)) operations.

AES algorithm steps is defined as:

Mix Column step is defined as:

Each column is treated as a polynomial over and is then multiplied modulo with a fixed polynomial.

.

Given equation explains Sub Bytes step :

(5)

VI. RESULTS

DF Raptor code is used for transmission error correction which has much better results than R-S code. Additional overhead

for required maximum level of protection is much lesser in case of DF Raptor code than that of R-S code. With Reed-Solomon

codes, however, a protection period of data must be segmented into multiple blocks that are protected individually, and the

resulting encoded blocks are then interleaved, at the cost of additional processing as well as reduced packet loss protection. For

same number of packets to be recovered, additional overhead for DF Raptor code is much lesser than reed-solomon code. Figure

3 considers a video stream with a media rate of 5 Mbps protected using a 500millisecond protection period. As

before, interleaved Reed-Solomon codes are necessary to protect the data over this protection period. Here,

however, with the symbol size and number of source symbols k held constant, different values of the number of

output symbols n are used for the DF Raptor and ReedSolomon codes to vary the protection amount. Figure 3

then shows the maximum randomly distributed packet loss rate that each code can tolerate with a mean time

between artifacts of at least 10,000 seconds (approximately every 2 hours and 46 minutes), where this level of

performance is considered by some to be a minimum requirement for commercial streaming video services.




Fig. 4 DF Raptor vs RS Code Algorithms

Results of Watermark and DF Raptor code improvement:

The multimedia can be corrupted by different noise attacks such as Gaussian noise (GN) with zero mean and

variance σ2 = 0.01,0.001,0.0001; Multiplicative noise with mean 0 and variance 0.1, 0.01, 0.001; Salt and pepper noise

addition with density of 10%, 10/0, 0.1%; Poisson noise; uniform noise adding. The goal is to find the corresponding

(correlated) pixel within a certain disparity range d (d E [0,….,dmax]) that minimizes the associated error and maximizes the

similarity.

Fig. 5 Average NC with Noise and DF Raptor code

The graph in fig. 5 shows that proposed system gives better Normalized Correlation (NC) than existing system for

Watermark and data recovery. Normalized Correlation (NC) is given by following equation:

NC = 1/N * sum{ [x(n) - mean(x)]* [y(n) - mean(y)] }/ (sqrt(var(x)*var(y)) (6)

where the sum is taken from 1 till N, mean(x) is the mean of x, var(x) is the variance of x, sqrt is square root.




VII. CONCLUSION

Proposed System provides strong security for multimedia data as compared to existing system. Digital Watermark in

proposed system provides authentication for image, video as well as audio data. DF Raptor Code improves performance of

protection from transmission errors and is powerful than Reed-Solomon Code.The analysis confirm that the proposed scheme

achieves good robustness against different attacks on multimedia data. In existing system, each cryptographic share has same

size as of input secret data and it increases space requirement. But in proposed system, storage space requirement reduces as

multimedia data is being split and then encrypted and uploaded on different cloud servers.A DF Raptor code’s processing

requirements are significantly less than that of a Reed-Solomon erasure code and grow only linearly with the source block size.

Additional overhead for required maximum level of protection is much lesser in case of DF Raptor code than that of R-S code.

Normalized Correlation in proposed system is much better than in existing system as DF Raptor code is used along with

Watermark and more symbols of original data are recovered in proposed system. Shares in proposed system are protected using

AES Encryption algorithm and it increases protection level in proposed system.

ACKNOWLEDGEMENT

With immense pleasure, I am presenting this Proposed System Work on “Cloud Data Security for Users" as a part of the

curriculum of M.E. Computer Engineering at G.S. MOZE COLLEGE OF ENGINEERING. It gives me proud privilege to

complete this Project Work under the valuable guidance of Prof. Mrs. Pradnya R. Patange. I am also extremely grateful to Prof.

Ratnaraj Kumar (HOD of Computer Department) and Principal for providing all facilities and help for smooth progress of

Project Work. I would also like to thank all the Staff Members of Computer Engineering Department, Management, friends and

my family members who have directly or indirectly guided and helped me for the preparation of this Project and gave me an

unending support right from the stage the idea was conceived.

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AUTHOR(S) PROFILE

Sujit Laxman Thorat, received B.E. degree in ComputerEngineering from Pravara Rural Engineering

College, Loni in Pune University and PG Diploma in DAC from IACSD, Pune in 2011 and 2012,

respectively. Currently, he is pursuing M.E. Degree from G. S. Moze College of Engineering, Balewadi,

Pune. Also, he is working as Software Developer in Opulent Infotech Pvt. Ltd. Company from October

2015.

Prof. Pradnya R. Patange, received B.E. degree in Computer Engineering from Govt. College of

Engineering, Karad. After graduation, she has completed M.Tech in IT Engineering from Bharati

Vidyapeeth College of Engineering, Pune. Now, she is working as Assistant Professor in I.T. Department

in G. S. Moze College of Engineering with 10 Years of experience.

Documents

Cloud Data Security for Usersijarcsms.com/docs/paper/volume4/issue6/V4I6-0099.pdf · infrastructure. So, it is more prone to security threats and vulnerabilities. At present, a major