International Journal of Computer Engineering and Applications, Volume X, Issue VI, June 16 www.ijcea.com ISSN 2321-3469 IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING ABSTRACT: Rucha D. Kulkarni and Dipak V. Patil G.E.S s R.H Sapat C.O.E. M.S. and R,Nashik,Savitribai Phule Pune University,India Relational data particularly shared extensively by the owners with research communities and in virtual data storage locations in the cloud, to work in a collaborative environment for knowledge extraction and decision making and also to make data openly available. Thus they are vulnerable to security threats concerning ownership rights and data tampering. Watermarking is used to ensure security related to ownership protection and tamper proofing in various data formats. While ownership protection using watermarking, the relational data gets modified so that the data quality gets compromised. Traditional watermarking techniques were unable to recover original data from watermarked data. Reversible watermarking is watermarking technique that tries to overcome the problem of data quality degradation by allowing recovery of original data along with the embedded watermark information. To ensure watermark encoding and decoding by using all the features in knowledge discovery and original data recovery in the presence of active malicious attacks reversible watermarking is used. Encryption of nonnumeric data is utilized as a contribution in the existing system which provides privacy preservation to both numeric as well as non-numeric data. Keywords: Reversible watermarking, genetic algorithm, ownership protection, data quality, encryption [1] INTRODUCTION In today s surge due to extensive growth of the Internet offers wide range of web services. Examples are e-commerce, decision support system, database, digital repositories and libraries etc. Relational data particularly used extensively by the cloud users and owners for research oriented work. The purpose is to share data that can be useful in collaborative environment and make data available to all so that it is useful for knowledge extraction and decision making. However these transparently accessible datasets make appealing focuses for attacks. Thus it is important, that in shared environments like cloud, security threats that arise from un-authenticated parties and relational databases are necessary to be addressed along with the application of ownership rights on behalf of their owners. Watermarking systems have broadly been utilized to guarantee security as far as ownership protection and sealing for a wide variety of data formats. Reversible watermarking strategies can guarantee information or data regeneration alongside ownership protection. Serial codes, data hashing, fingerprinting [1] are some alternative techniques used for ownership protection. A Rucha D. Kulkarni, Dipak V. Patil 1
IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING transactional watermark which is also known as fingerprinting that are used to guide and identify digital ownership by watermarking all the replicas of contents with different watermarks for different recipients. Basically this sort of advanced watermarking tries to recognize the root of data emission by tracking criminal operators. Watermarking has the property that it can serve protecting ownership over the digital content by marking the information with a watermark exclusive to the owner. The embedded watermark can afterwards be used for proving and claiming ownership. Reversible watermarking tries to defeat the issue of information quality down gradation by permitting regeneration of unique information alongside the embedded watermark data. Proposed framework shows such reversible watermarking method that keeps the information valuable for learning revelation. Figure: 1. General block diagram of watermarking for database [Figure-1] shows general structure of watermarking process. In this process original data gets processed through watermarking algorithm. While processing original data watermark gets embedded into it, at the end system will return recovered data. [2] LITERATURE SURVEY Watermarking database relations is a area which demand research focus owing to the commercial implications of database theft. Digital watermarking for relational databases emerged as a candidate solution to provide problems regarding security such as copyright protection, tamper detection etc. Many watermarking techniques have been proposed in the previous work to address these purposes. Digital watermarking of multimedia content is more commonly acknowledged, particularly image watermarking. Some reversible digital watermarking techniques related to proposed system is mentioned below. Reversible watermarking for image: Adnan M Alattar [3] proposed multimedia reversible watermarking. They proposed algorithm that based on difference expansion of colored images. Since the watermark is completely reversible, the original image can he recovered exactly. Ingemar J. Cox[4] presented a secure algorithm for 2
International Journal of Computer Engineering and Applications, Volume X, Issue VI, June 16 www.ijcea.com ISSN 2321-3469 watermarking images, and a methodology for multimedia watermarking that may be derived for audio, video, and multimedia data. Irreversible relational database watermarking: Agrawal and kierman[5] proposed first well understood conventional irreversible database watermarking plan for watermarking relational databases for numeric qualities. The system survives a few assaults and which safeguards mean variance of all numerical characteristic. This plan can t be straightforwardly applied to watermarking categorical.to settle this issue Sion, R [6] presented a one of a kind technique for rights preservation for categorical information through watermarking to fix this issue. This plan includes watermarking of categorical property by changing some of its quality to other worth to the property if such change is bearable in certain applications. Reversible relational database watermarking: Zhang et al. [7] proposed first reversible watermarking of relational database to accomplish less also correct verify of relational databases by means of expansion on data error histogram. This strategy has distributive mistake inside of two uniformly circulated variables as some beginning nonzero digits of errors to manufactured histograms. Gaurav Gupta and Josef Pieprzyk [8] proposed an change over the reversible and blind watermarking plan introduced, identifying and wiping out a basic issue with the past methods related with reversible watermarking. The proposed plan gives high security against secondary watermarking Attacks. Jung-Nan Chang and Hsien-Chu Wu [9] proposed strategy that recognizes database altered by installed significance describe of the innovation of database. The affiliation guideline of frequent pattern tree information mining is utilized to recognize the relationship existing with the protected attribute and others too in the database. Khurram Jawad, Asifullah Khan [10] presented new robust procedure for reversible watermarking approach for the security of relational databases. This methodology depends on the thought of difference expansion as on using genetic algorithm (GA)to enhance watermark limit and to reduce distort error. M. E.Farfoura and S.- J. Horng, [11] introduced a novel blind reversible watermarking strategy that guarantees us the responsibility for in the range of Relational Database of watermarking. In the proposed technique D. M.Thodi and J. J. Rodriguez [12] proposed an optional solution to distortion by using histogram shifting technique. The technique enhances distortion performance at low embedding limit. To enhance this new strategy was exhibited called prediction error expansion. Mahmoud E. Farfoura,Shi-Jinn Horng an,et.al [13] designed and used a confirmation protocol in view of an productive time-stamp convention, and they proposed a visually impaired turning around watermarking technique that guarantee assure ownership in the territory of relative database watermarking. Erik Sonnleitner [14] proposed a watermarking algorithm in based on parameter that tuple dividing watermarking and white spaces while utilizing it as a part of open watermark. The watermarking plan is non intrusive resilient also oblivious to reversibly and its suitable for databases of any sizes with sensible exhibitions on inserting and extraction. Javier Franco-Contreras, Gouenou Coatrieux,et.al[15] proposed the robust reversible watermarking modified by initially proposed under Vleeschouwer et.al for the pictures assurance of given relational databases. The propose plan states relative precise position of the circular histogram centric mass of one numerical quality for message showing and installing. Various encryption methods for nonnumeric data are available in [19]. Rucha D. Kulkarni, Dipak V. Patil 3
IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING [3] PROPOSED SYSTEM The proposed framework is based on work by Saman Iftikhar et al.[1] which is for numeric data, along with this we have proposed to add ownership right protection for non-numeric data. In this system the most part focuses on reversible watermarking of relational database which authorizes ownership rights and gives intends to handling information altering without compromising information quality and recovers unique information after watermark decoding. In this framework watermark creation is done utilizing Genetic algorithm as an evolutionary technique. The proposed framework mainly contains following phases: 1) Preprocessing: In the preprocessing phase, two important tasks are accomplished: a) Selection of a suitable component for watermark embedding: For developing a huge data model of different elements of the dataset, every one of the components are positioned by their impact in data extraction, in light of their mutual dependence on different elements. For this reason, mutual information (MI), is exploited that is a imperative statistical measure for calculation of shared dependence of two arbitrary variables. Mutual information is calculated using following equation, Let, P A(a)- Probability of tuple a in feature A, P B(b)- Probability of tuple b in feature B, P AB(a,b)- Joint probability and MI(A,B)-measures degree of correlation of feature by measuring probability distributions Figure: 2. Equation to calculate Mutual Information b) Calculation of an optimal watermark with the help of an optimization technique: For the making of ideal watermark data that should be inserted in the first information we utilize a evolutionary method Genetic Algorithm. In the search of optimal solution, the GA follows an iterative mechanism to expand a population of chromosomes. The GA preserves essential data utilizing the application of essential genetic operations to these chromosomes that include selection, crossover, mutation and replacement. The GA inspected the nature of every applicant chromosome by exploiting a fitness function. The evolutionary system of the GA over and again proceeds through various eras, until some finish criteria is met. The fitness function can be mathematically expressed as following in which A indicates one of the feature in set of features F max β A...(2) where, AϵF, A 2) Watermark encoding: In this stage watermark has been embedded in selected feature. The watermark is embedded into each tuple for the selected feature of the dataset. The information owner can choose any number of elements for watermark implanting based upon a secret limit and Mutual 4
International Journal of Computer Engineering and Applications, Volume X, Issue VI, June 16 www.ijcea.com ISSN 2321-3469 data of the elements. The watermark encoding algorithm begins the embedding process with the most significant bit MSB of the watermark. To accomplish this algorithm works with one tuple at once. Watermark gets encoded in selected feature, after creation of watermark is completed. While encoding watermark into tuples of selected feature first it is checked that which chromosome is optimal and optimum value of β. After finding optimum value of _ parameter _r that denotes percentage change in watermark encoding is calculated using equation given as follows where WEn denotes watermark encoder. η r = D r * WEn...(3) Figure: 3. System Architecture Algorithm Input: original database, watermark bits, optimized value to watermarked feature Output: Watermarked database, matrix containing percent change in data values 1. for all watermark bits of length l do 2. for all tuples of data 3. if watermark bit is 0 then 4. Compute changes using equation (3) Rucha D. Kulkarni, Dipak V. Patil 5
IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING 5. Watermark data using D Wr = D r + β 6. if watermark bit is 1 then 7. Compute changes using equation(3) 8. Watermark data using D Wr = D r - β 9. End if 10. End for 11. Else if non-numeric data, do encryption using AES algorithm 12. End if 3) Watermark decoding: In the watermark decoding process, the initial step is to recognize the elements which have been checked. The procedure of optimization through GA is not required at this stage. We utilize a watermark decoder which computes the measure of progress in the estimation of an element that does not change its information quality. The watermark decoder translates the watermark by working with one piece at once. In watermark decoding phase very first task is to locate features which have been marked. WDe watermark decoder is used to calculate amount of change in value of feature that won t affect to data quality. Percentage change which is denoted by _dr is calculated as follows: η Δr = D w * WDe...(4) η Δr = η dr η r...(5) where, η Δr = The difference between the changes detected in the value of a feature during the encoding and decoding Algorithm Input: Watermarked database, matrix containing percent change in data values, length of watermark Output: Decoded watermark 1. for all tuples of data do 2. for all watermark bits of length l 3. Detected amount of change is computed using equation (4) 4. Difference in detected changes using equation (5) 5. if difference in detected changes 0 then 6. Detected watermark bit is 1 7. Else if if difference in detected changes 0 and 1 then 8. Detected watermark bit is 0 9. End if 10. End for 4) Data recovery: After identifying the watermark string, a few post processing steps are performed for error correction and data recovery. The optimized value β of calculated through the GA is used for reconstruction of original data using following equations: D r = D Wr + β...(6) D r = D Wr - β...(7) Algorithm 6
International Journal of Computer Engineering and Applications, Volume X, Issue VI, June 16 www.ijcea.com ISSN 2321-3469 Input: Watermarked database, watermark bits Output: recovered database 1. for all tuples of data do 2. for all watermark bits of length l 3. if detected watermark bit is 1 then 4. Data is recovered using equation (6) 5. Else use equation (7) 6. End if 7. End for 8. Else if non-numeric data, apply decryption [4]. EXPERIMENTAL SETUP AND RESULTS Heart disease dataset from UCI repository [16] is used for this experiment. Table I shows description of classes and instances, where each class refers to heart disease. It has 920 number of instances and there are 76 attributes of which 15 are used for experiment.14 are numeric and 1 non-numeric. Attribute names are: Name, Age, sex, cp, trestps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, num. Class Instances Cleveland 303 Hungarian 294 Switzerland 123 Long 200 Beach VA TABLE I: Heart disease dataset Mutual Information (MI)[17],[18] is a popular information concept that calculates amount of details one feature has about other feature in dataset statistically. MI identifies features that don t have any significant effect on decision making process. Thus MI is an important statistical measure for computation of mutual dependence of two variables. The Table II show the results we obtained after calculating mutual information between age and each other attribute in the dataset. So, the MI of age is blank. Likewise mutual information is calculated between every feature with all other features. For determining data quality MI is used as measure. Attributes MI O MI W MI Age - - Sex 0.1252 0.1252 0 Rucha D. Kulkarni, Dipak V. Patil 7
IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING Cp 0.4264 0.4264 0 Trestbps 1.9973 1.9973 0 Chol 3.9252 3.9252 0 Fbs 0.1112 0.1112 0 Restecg 0.1782 0.1782 0 Thalach 3.0984 3.0984 0 Exang 0.1426 0.1426 0 Oldpeak 0.4029 0.4029 0 Slope 0.2265 0.2265 0 Ca 0.3899 0.3899 0 Thal 0.2433 0.2433 0 Num 0.3689 0.3689 0 TABLE II: Mutual Information of Selected Feature Before and After Watermarking The quality of proposed system is also evaluated using other statistical measures like mean and variance. Table III shows mean and variance before and after watermarking. Mean O Variance O Mean W Variance W 39.7428 48.3485 39.8533 49.0744 40.7142 51.3763 40.2133 52.0038 42.8000 41.5733 41.5733 42.2588 TABLE III: Mean and Variance before and after watermarking. [6] CONCLUSION The proposed system is a system to protect ownership rights of relational database. The system creates watermark using genetic algorithm as an optimization technique, is able to protect ownership rights of relational data along with robustness without compromising data quality. The system has exploited GA as a optimization technique. GA applied on current problem domain provides global optimal solution. Irreversible watermarking techniques make changes in the data to such an extent that data quality gets compromised. Proposed reversible watermarking technique is used to cater to such scenarios because this is able to recover original data from watermarked data and ensure data quality to some extent. Technologies and techniques mentioned in proposed system have been adapted and integrated in order to build the overall system for ownership rights protection of relational database. In 8
International Journal of Computer Engineering and Applications, Volume X, Issue VI, June 16 www.ijcea.com ISSN 2321-3469 addition with this watermarking of numerical data we are also providing privacy preservation to nonnumeric data. REFERENCES [1] Saman Iftikhar, M. Kamran, and Zahid Anwar, RRW-A Robust and Reversible watermarking Technique for Relational Data IEEE transactions on knowledge and data engineering, vol. 27, no. 4, april 20 [2] Raju Halder,Shantanu Palet.al, Watermarking Techniques for Relational Databases: Survey, Classication and Comparison Journal of Universal Computer Science, vol. 16, no. 21 (2010) [3] A. M. Alattar, Reversible watermark using difference expansion of triplets, in Proc. IEEE Int. Conf. Image Process., 2003, pp. I-501,vol. 1. [4] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, Secure spread spectrum watermarking for multimedia, IEEE Trans. Image Process.,vol. 6, no. 12, pp. 16731687, Dec.1997. [5] R. Agrawal and J. Kiernan, Watermarking relational databases, in Proc. 28th Int. Conf. Very Large Data Bases, 2002, pp. 155-166. [6] Sion, R.Proving ownership over categorical data. In Proceedings of the 20th IEEE international conference on data Engineering ICDE, April 2004 (pp. 584596). [7] Y. Zhang, B. Yang, and X.-M. Niu, Reversible watermarking for relational database authentication, J. Comput., vol. 17,no. 2, pp. 59-66, 2006. [8] 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. [9] J.-N. Chang and H.-C. Wu, Reversible fragile database watermarking technology using difference expansion based on SVR prediction, in Proc. IEEE Int. Symp. Computer., Consum. Control, 2012, pp. 690-693. [10] K. Jawad and A. Khan, Genetic algorithm and dierence expansion based reversible watermarking for relational databases J. Syst. Softw., vol. 86, no. 11, pp. 2742-2753, 2013. [11] M. E. Farfoura and S.-J. Horng, A novel blind reversible method for watermarking relational databases, in Proc. IEEE Int. Symp. Parallel Distrib. Process. Appl., 2010,pp. 563569. [12] D. M. Thodi and J. J. Rodriguez, Prediction-error based reversible watermarking, in Proc. IEEE Int. Conf. Image Process. 2004, vol. 3, pp. 1549-1552. [13] M. E. Farfoura, S.-J. Horng, J.-L. Lai, R.-S. Run, R.-J. Chen, and M. K. Khan, A blind reversible method for watermarking relational databases based on a time-stamping Syst.Appl., vol. 39, no. 3, pp. 3185-3196, 2012. [14] E. Sonnleitner, robust watermarking approach for large databases, in Proc. IEEE First AESS Eur. Conf. Satellite Telecommun.,2012, pp. 1-6. [15] Javier Franco-Contreras, Gouenou Coatrieux, Robust Lossless Watermarking of Based on Circular Histogram Modulation,IEEE Transactions on information forensics and security, Vol. 9, No. 3, March 2014 [16] K. Bache and M. Lichman. (2013). UCI machine learning repository [Online]. Available:http://archive.ics.uci.edu/ml [17] T. M. Cover and J. A. Thomas, Elements of Information Theory. New York, NY, USA: Wiley- Interscience, 2012. Rucha D. Kulkarni, Dipak V. Patil 9
IMPLEMENTATION OF OWNERSHIP RIGHTS PROTECTION FOR NUMERIC AND NON-NUMERIC RELATIONAL DATA USING [18] Huan Liu,Hiroshi Motoda,Computational Methods of Feature Selection. william stalling,cryptography and network security 4 th edition. Author[s] brief Introduction Rucha D. Kulkarni Is pursuing the Masters in Computer from G. E. S. R. H. Sapat College of Engg., Nashik under Savitribai Phule Pune University.She has pursued her Bachelor s Degree in IT from K.K.W.I.E.E.R., Nashik under Savitribai Phule Pune University. Dipak V. Patil received B.E. degree in computer engineering in 1998 from University of North Maharashtra India and M.Tech. degree in computer Engineering in 2004 from Dr.B. A. Technological University, Lonere, India. He has done Ph.D. degree from S. R. T. M. University, Nanded. Currently he is an Associate Professor in Computer Engineering Department at G.E. S. R. H. Sapat College of Engg., Nashik,India.His research interests are in data mining and soft computing. 10