International Journal of Modern Trends in Engineering and Research www.ijmter.com e-issn No.:2349-9745, Date: 28-30 April, 2016 Watermarking Technology for Relational Database Priyanka R. Gadiya 1,Prashant A. Kale 2 1 Computer engineering, Late G.N. Sapkal College of Engineering Nasik, priyankagadiya89@gmail.com 2 Computerengineering,Late G.N. Sapkal College of Engineering Nasik, prashantkale15@gmail.com Abstract-In the digital world of today, data is excessively being generated due to the increasing use of the Internet and cloud computing. Todays improvised information technology is playing an vital role in the increased use of information systems comprising relational database. Relational database is shared broadly by the proprietor with research communities and in virtual data storage locations in the cloud. Watermarking is advocated to enforce ownership rights over shared relational data and for providing a means for tackling data tampering reversible watermarking scheme possesses the ability of perfect restoration of the original attribute data from the untampered watermarked relational databases. However, such techniques are usually not robust against malicious attacks and do not provide any mechanism to selectively watermark a particular attribute by taking into account its role in knowledge discovery. Therefore, reversible watermarking is required that ensures; (i) watermark encoding and decoding of all the suitable feature by accounting their role in decision making; and, (ii) data recovery also in the presence of active malicious attacks. Keywords-reversible watermarking, data recovery, data quality, robustness, relational database @IJMTER-2016, All rights Reserved I. INTRODUCTION In today s world requirement to access web or online network and cloud computing causation for the wide generation of database.effective improvement and easy access to information technology also responsible for the wide use of information system that affects relational database. The heritor shared extensively particular database with the research association and also over the cloud. These work perform insight to offer collective surrounding and make data openly available so that it used for knowledge abstraction and organization decision purpose. Nowadays, over the distributed environment of organization and organization working in the collaborative environment sharing database over online is an important job for such organizations and for research purpose too, which also involves buying/selling of databases. For example, collaborative use of database related to scientific, atmosphere, healthcare and medical, scientific, stock market, customer behavior, etc. As they can be assailable as per security concerning heritor rights and data tampering. Watermarking is technique which perform encoding of signal in such way that heritor rights over shared relational database and a means for deal with data alteration. Watermarking offers solutions for various problems occurs in the sharing of different multimedia objects such as, audio image, video, text, and as well as for the relational data. The watermarking technique may causes modification or alternation of underlying data at the certain amount which diminish data quality[2].solution to such problem is advancement in watermarking i.e. reversible watermarking which employed results in quality and exact authentication of relational data. This reversible watermarking technique assures the exact restoration of the original attribute database from the watermarked relational databases [3]. Watermarked database may threaten by malicious attack which may cause deletion, alteration, or false insertion. The advancement i.e. robust watermarking scheme possesses the exact recovery also in the presence of malicious attack [1]. The additional functionality provided which involves the selective watermarking of specific attribute which provide means to selection of suitable feature for the encoding through the watermarking technique
II. RELATED WORK 2.1 Histogram expansion based watermarking: Yong Zhang1,2,*, Bian Yang3, [3] initiates this technique in which introduces that the real values have an odd distribution over a variable range of [a-b,b-a]. To make the implementation with reduce complexity histogram expansion in this real value case, we extract just particular portion of each original real value ( partial real value) instead of the whole one, and then retrieve the digits resident in the original value order of each error occurred from two adjacent original real values to form the final histogram. 2.2 Difference expansion watermarking technique: G. Gupta and J. Pieprzyk [4], proposed a watermarking scheme that is blind as well as reversible. The utilization of difference expansion on integers to achieve reversibility. The Difference expansion watermarking techniques [5], [6] accomplished way of arithmetic operations on numeric features and deliver transformations. The watermark information is usually encoded in the LSB of function of relational databases to optimize distortions. The major advantages offered by this technique are reversibility to high quality original database, rightful proprietor authentication, robust against secondary watermarking attacks, and no need of secure secondary storage for restoring original database. 2.3 Difference Expansion and Support Vector Regression Prediction: Jung-Nan Chang and Hsien-Chu Wu [7] initiates the use of FP-tree data mining for the improvised choosing of valuable features in the database table. Then, the correlated valuable features of the secured field in the mining results are farther trained by SVR for predicting the secure field. So, the computation of each record required for the calculation of a difference between the predicted value and the original value of the secure field. Using this objective value and the LSB encoding scheme, the result is upgraded into the table. Figure1. Diagram of SVR Training and Embedding Procedure 2.4 Prediction-Error Expansion Watermarking Technique: Xiaolong Li, Bin Yang, and TieyongZeng [8] proposed Prediction-error expansion (PEE) technique which introduce for reversible watermarking that can encode broad payloads into digital images with tiny distortion. The encoding and extraction are two steps in which PEE executed. In PEE[9][10][11][12][13], The prediction algorithm used for the prediction of each bit. Then, scan image pixels from left to right and top to bottom, and repeat the Expansion embedding Histogram shifting operation until the message is embedded. So, to optimize the distortion in PEE, the capacity- @IJMTER-2016,All rights Reserved 198
parameter is taken as the tiny integer such that the innerregion can provide enough expandable pixels to embed the payload. III. PROPOSED SYSTEM All the Methods like Histogram expansion based watermarking, DEW,PEE, GAPEW that are discussed in the literature survey have some drawbacks. The Genetic Algorithm based reversible and robust watermarking system provides the quality data recovery after watermarking even in the presence of malicious attack. The watermarking scheme possesses the following main modules: 1) Watermark preprocessing 2)Watermark insertion 3)Watermark extraction and 4) Data recovery. Figure 2. Architecture of Watermarking In the preprocessing phase, following task are performed: (1) selection of a function for watermarking which is selected on the basis of mutual information.the feature(s) having lower MI (mutual information) can be selected for watermarking. With consideration the attacker can try and predict the feature with the lowest MIin an order to guess which feature has been watermarked. (2) optimalwatermark is calculated with the use optimization technique i.e. genetic algorithm. These parameter are further used for the watermark insertion and watermark extraction phase. After the preprocessing in watermark insertion;information embedded in such way that accuracy of data will not degrade. Using the watermark bit the tuple selected in preprocessing phase are encoded. The watermark encoder encodes the database by working with one bit at a time. In the watermark extraction the watermarked information is decoded. The watermark decoder performs decoding of the watermarked database by working with one bit at a time. Decoding perform on watermarked database with the help of a matrix which consist change of percentage in data values and knowing the length of watermark bit. In Contribution we will perform encoding and decoding on characters as in base paper it is applied to numeric fields only. In proposed system ASCII value of data will get changed for every value in data field. I)For each 0 in generated l bit string, a constant value α is added to ASCII value of the last letter for all tuples in selected feature. II)For each 1 in generated l bit string, a constant value α is subtracted from the ASCII value of the last letter for all tuples in selected feature. @IJMTER-2016,All rights Reserved 199
The data recovery phase is responsible for the error correction as well as recovery of information in case of malicious attack occur too. Input: Original Database Output: Recovered database with data quality Begin: 1) Features are selected for watermarking 2) Optimum watermark bit is generated by genetic algorithm 3) Using optimum watermark bit watermark encoding is performed 4) Watermarked is extracted 5) Data recovery operation is performed. End The system uses data set to perform the watermarking, Cleveland Heart Disease dataset, MAGIC Gamma Telescope dataset and PAMAP2 PhysicalActivity Monitoring dataset. CONCLUSION By observing the various models of watermarking, we can conclude that they provide scheme for watermarking but still there is scope of improvement. The use of genetic algorithm(ga) provides the optimum watermark information that is required for the encoding of original data. The some models offersreversible watermarking that is a special type of data embedding. Being reversible; means the original digital content can be fully restored after data decoding. But such model is not degrades their performance when malicious attack occurs. The model based on GA provides the semi-blind and robust reversible watermarking for the relational database.. ACKNOWLEDGMENTS With all respect and gratitude, I would like to thank all people who have helped us directly or indirectly for the paper presentation. I would like to thank my guide, Prof. P.A. Kale, for her guidance and support. I will forever remain grateful for the constant support and guidance extended by guide, in making this paper. Through our many discussions, he helped me to form and solidify ideas. The invaluable discussions I had with him, the penetrating questions he has put to me and the constant motivation, has all led to the development of this report. REFERENCES [1] Saman Iftikhar, M. Kamran, and Zahid Anwar, RRW A Robust and Reversible Watermarking Technique for Relational Data in IEEE Transon Knowledge and Data Engineering, VOL 27, NO. 4, April 2015 [2] R. Agrawal and J. Kiernan, Watermarking relational databases, in Proc. 28th Int. Conf. Very Large Data Bases, 2002, pp. 155 166 [3] Y. Zhang, B. Yang, and X.-M.Niu, Reversible watermarking for relational database authentication, J. Comput., vol. 17, no. 2, pp. 59 66, 2006. [4] G. Gupta and J. Pieprzyk, Reversible and blind database watermarking using difference expansion, in Proc. 1st Int. Conf. Forensic Appl. Tech. Telecommun., Inf., Multimedia Workshop, 2008, p. 24. [5] A. M. Alattar, Reversible watermark using difference expansion of triplets, in Proc. IEEE Int. Conf. Image Process., 2003, pp. I 501, vol. 1. [6] G. Gupta and J. Pieprzyk, Database relation watermarking resilient against secondary watermarking attacks, in Information Systems and Security. New York, NY, USA: Springer, 2009, pp. 222 236. [7] K. Jawad and A. Khan, Genetic algorithm and difference expansion based reversible watermarking for relational databases, J. Syst. Softw., vol. 86, no. 11, pp. 2742 2753, 2013. [8] X. Li, B. Yang, and T. Zeng, Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection, IEEE Trans. Image Process., vol. 20, no. 12, pp. 3524 3533, Dec. 2011. @IJMTER-2016,All rights Reserved 200
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