Watermarking Scheme for Numerical and Non-Numerical Relational Database

Watermarking Scheme for Numerical and Non-Numerical Relational Database Priyanka R. Gadiya 1, Prof. P.A.Kale 2 priyankagadiya89@gmail.com 1, prashantkale15@gmail.com 2 M. E. Student, Dept. of Computer Engg. Late G.N.Sapkal COE, Nashik, Maharashtra, India 1 Assistant Professor, Dept. of Computer Engg. Late G.N.Sapkal COE, Nashik, Maharashtra, India 2 Abstract Digitization and easy access to the internet leads to the excessive generation of data. The relational data is to be shared over internet, cloud for the purpose of research, business and decision making. The watermarking is technique which provides the protection to the database by embedding information. But during embedding data causes alternation which affect quality of data. Data recovery with the intended data quality is assured using the Reversible Watermarking. Robust Watermarking ensures the recovery against the attack that causes false insertion, alternation, deletion of the relational databases. As the relational database comprised of both numerical as well as non-numerical data so for watermarking both should be considered. Keywords- Robust watermarking, Reversible watermarking, non-numerical, numerical, data quality 1. INTRODUCTION In the todays digital world due to rapid growth of the Internet, the extensive growth of digital contents, and the easier distribution exclusive rights fortification of owners is becoming more and more necessary [1].Data is generated in the various formats like text, video, image, audio and relational data. Sharing data online over the internet or virtual storage space is become vital activity for organization and, which may include importing and exporting of relational data[2]. While databases are susceptible to security intimidations therefore it is essential to enforce the copyright of digital asset for tackling data interfering. Watermarking is technique which advocated Copyright over shared relational data and for providing a means of deal with data alteration. Watermarking is method of embedding data in such form that is not readily available to the unauthorized user. It allows the user to add a layer of security to the database content by recognizing exclusive rights of owner. The watermarking technique which are irreversible may causes modification of data undergoes through it at the certain extent [3].Unlike encryption or hashing technique i.e. alternative solution to security of databases content, watermarking scheme modify original database as a modulation of the watermark information, and causes impossible to prevent permanent distortion to the original database, and affects to meet the integrity requirement of many application[4]. To improve such scenario reversible watermarking is used which results in accurate and exact authentication of relational databases. This reversible watermarking technique acquires the capability of accurate rebuilding of the original information from the watermarked databases [4]. Watermarking can be threaten of malicious attack which may effect as alteration, deletion, or false insertion. The robust watermarking scheme possesses the exact recovery in the existence of active malicious attack [5]. The supplementary function is evolved that uses selective watermarking of necessary specific attribute which involves selection of suitable feature for the embedding through the watermark. The appropriate selection of the attribute for encoding provides the improvised security concern. During the feature selection it considers numerical as well non-numerical features. 2. RELATED WORK I) Histogram expansion based watermarking: This technique initiates by Yong Zhang1,2,*, Bian Yang3, [3] in which introduces that the real values over a particular variable range of [a-b,b-a] has an odd distribution. To decrease the complexity of 8

implementation in the histogram expansion this real value case, partial real value is used instead of the whole one which is extraction of just particular portion of each original real value. Then retrieve the digits in the original value of each error occurred where two adjacent original real values to form the final histogram. The capacity in the in this scheme is calculated between two neighbouring original partial values from the partial errors. II) Difference expansion watermarking technique: G. Gupta and J. Pieprzyk [5], proposed a watermarking scheme that is not only blind but also reversible. The application of difference expansion on the integers for achieves reversibility. The Difference expansion watermarking techniques [6], [7] accomplished way of arithmetic operations on numeric features and deliver transformations. The major advantages offered by this technique are reversibility to high quality original database, lawful owner authentication, robustness against secondary watermarking attacks, and no need of secure secondary storage for reinstating database 3. PROPOSED WORK All the Methods like Histogram expansion based watermarking, DEW, PEE, GAPEW that are discussed in the literature survey have some drawbacks. The 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 4) Data recovery. III) Difference Expansion and Support Vector Regression Prediction: Jung-Nan Chang and Hsien-Chu Wu [8] initiates the use of FP-tree data mining for the improvised choosing of valuable features in the table. Then, the related valued features of the secured field in the mining results are farther trained by SVR for forecasting the secure field. So, the calculation 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. IV) Genetic algorithm and difference expansion technique(gadew): Genetic Algorithm is used to improve the capacity of DEW in databases, while distortion tolerance is fixed. GA offers some randomness in DEW technique that makes it complex to predict attributes for attacker. Security is also improved by minimizing the distortion and reducing abrupt changes occurred by DEW. This is acknowledge by two measures added in the fitness function of GA, first by using the knowledge of the neighbouring values of the database, and second by reducing the distortion introduced by selecting attributes resulting in least distortion [8]. Fig.1. System Architecture 3.1 Watermark Preprocessing 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) than a particular secrete threshold specified by data owner can be selected for watermarking. With consideration the attacker can try and predict the feature with the lowest MI in an order to guess which feature has been watermarked. It keeps the important feature i.e. high MI feature protected. (2) Optimal watermark is calculated with the use optimization technique i.e. Genetic Algorithm. Genetic algorithm provides i) optimum chromosomal string ii)ß value. 9

These parameter are further used for the watermark insertion and watermark extraction phase. 3.1.1 Genetic Algorithm 1. Determine the number of chromosomes and mutation rate and crossover rate value. 2. Generate chromosome and the initialization value of the genes chromosome. 3. Process steps 4-7 until the number of generations is met. 4. Objective function value for each chromosome. F_Obj (chromo) = Abs (chromo) Abs (chromo) = Sum (genvaltotalgenval) 5. Fitness of each chromosome. Fitness (chromo) = 1/ 1+Fobj (chromo) Total = sum (fitness of all chromo) Probability (chromo) = Fitness (chromo)/ Total. 6. Crossover 7. Mutation 8. New Chromosomes (Offspring) Calculate Objective Function F_Obj (chromo)=abs(genvalue) 9. Solution; Best Chromosomes( chromo) After n iteration final value at selected chromosome will be best chromosome. 3.2 Watermark insertion After the preprocessing in watermark insertion; information embedded in such way that accuracy of data will not degrade. Using the chromosomal string the tuple selected in preprocessing phase is encoded. The percentage change is stored in matrix which is useful in extraction phase. It is required the watermark bandwidth should be enough large to assure robustness but not so big that it extinguishes the quality of data.the watermark encoder encodes the database by working with one bit at a time. For the numerical data it is encoded by adding chromosomal string.but for the string first its ASCII value is generated then encoded as the chromosomal string in numerical (binary) form. Then after encoding it converted back in the string. 3.3 Watermark extraction In the watermark extraction the initial step is to detect the attribute which have been watermarked. In this phase 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. Watermark bit is detected from every tuple. And then final watermark string is retrieved using the Majority Voting Scheme which is responsible for providing the robustness against the malicious attack at the certain extend. After the extraction as a output we get the original chromosomal string which is used before in the encoding. 3.4 Data recovery After watermark string is detected, in this phase original data is recovered. 3.5 Algorithm Input: Original Database Begin: 1. Features are selected for watermarking base upon mutual information (MI) according to specified threshold. MI(A,B)= PAB(a,b) log P AB(a,b) /P A(a) P B(b). a b 2. Optimum watermark bit and value of ẞ is generated by Genetic algorithm. 3. Using optimum watermark bit watermark encoding is performed over the feature selected by MI. a) If watermark bit is 1, then value of ẞ subtracted. b) If watermark bit is, then value of ẞ added. 4. After the encoding the database is place over the channel. 5. Watermarked decoding i) first find the feature which is watermarked ii) then bits are decoded based upon percent change values of watermark data from each tuple iii) Then final watermark string is detected by using majority voting scheme 6. Data recovery operation is performed using the optimum value generated by the genetic algorithm. a) If watermark bit is 1, then value of ẞ subtracted. b) If watermark bit is, then value of ẞ added. End Output: Recovered database with data quality. 4. EXPERIMENTAL RESULT 4.1Experimental setup The algorithm is implemented in.net Framework 3.5 using c#. To verify the result of the proposed system output database values is compared with original input database values. 51

Table 2. Watermark Encoding with Bits 1, started from First Bit() value of ẞ=.31 A1 Existing System(Numerical) Proposed System(Numerical + Non-numerical) 62 62.31 62.31 68 68.31 68.31 81 81.31 81.31 73 73.31 73.31 state-gov Not Applicable uvcvg/iqx private Not Applicable rtkxcvg bachelors Not Applicable cbdifopst Self-emp-inc Not Applicable Ugnh/gor/kpe Fig.2. Comparison of Recovered Data with Original data 4.2 Datasets 4.2.1. Cleveland Heart Disease dataset: This dataset incudes information about heart disease which consist of 12 instances having disease of, instances without disease of heart and each of that instance is described by 13 attributes. 4.2.2. PAMAP2 Physical dataset: The PAMAP2 is a dataset of Monitoring Physical Activity which has data of 18 differentiate physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. 4.3 Result Table Table 1 Selected Features Mutual Information before and after Watermarking Sr_no Name of feature MI O MI W MI 1 Age.8796.8796 2 Capital_gain.1537.1537 3 Capital_loss.2767.2767 4 Education.4279.4279 5 Education_num.4279.4279 6 Hours_per_week.3689.3689 7 Marital _Status.1537.1537 8 Native_country.2566.2566 9 Occupation.4886.4886 1 Race.1537.1537 11 Gender.6447.6447 12 Relationship.238.238 The attacks are categories as Data Insertion, Data Alternation and Data Deletion Attack Data Insertion 1 3 7 9 Insert Tuple(%) Fig.3. Data Recovery after Data Insertion attack Data Alternation 1 3 7 9 Alter Tuple(%) Fig.4. Data Recovery after Data Alternation attack 511

[3] R. Agrawal and J. Kiernan, Watermarking Data Deletion relational databases, in Proc. 28th Int. Conf. Very Large Data Bases, 22, pp. 155166 [4] Y. Zhang, B. Yang, and X.-M. Niu, Reversible watermarking for relational database authentication, J. Comput., vol.17, no. 2, pp. 5966, 26. 1 3 7 9 Deleted Tuples(%) Fig.5. Data Recovery after Data Deletion attack [5] Saman Iftikhar, M. Kamran, and Zahid Anwar, RRW A Robust and Reversible Watermarking Technique for Relational Data in IEEE Trans on Knowledge and Data Engineering, VOL 27, NO. 4, April 215 5. CONCLUSION AND FUTURE SCOPE The Watermarking is method of embedding data in order to provide the layer of security. Irreversible watermarking scheme performs encoding may causes the degradation of data quality. To cater such scenarios Reversible watermarking techniques are used; being reversible means the quality of original digital content is ensured. However, these techniques performance is improved by Robust Watermarking by providing the robustness against malicious attack specially that attack that affects some particular tuple. Here rather watermarking the entire database the particular feature is selected according criteria (MI value) which saves time and cost. In this paper, unlike the previous system nonnumerical (string) are also considered for watermarking as the both numerical and nonnumerical may consist the important and sensitive information. Future work will include the watermarking over distributed system i.e. shared data. 6. ACKNOWLEDGMENTS [6] 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, 28, p. 24. [7] A. M. Alattar, Reversible watermark using difference expansion of triplets, in Proc. IEEE Int. Conf. Image Process.,23, pp. I1, vol. 1. [8]G. Gupta and J. Pieprzyk, Database relation watermarking resilient against secondary watermarking attacks, in Information Systems and Security. New York, NY, USA: Springer, 29, pp. 222236. [9] K. Jawad and A. Khan, Genetic algorithm and difference expansion based reversible watermarking for relational databases, J. Syst. Softw., vol. 86, no. 11, pp. 27422753,213. [1] X. Li, B. Yang, and T. Zeng, Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection, IEEE Trans. Image Process., vol. 2,no. 12, pp. 35243533, Dec. 211. I am thankful to Prof P.A. Kale Assistant professor in the Department of Computer Engineering in Late G.N. Sapkal College of Engineering, Nasik. For providing constant guidance and encouragement for this research work. 7. References [1] M.E. Farfour, S.-J. Horng, J.-L.Lai, R.-S Run, R.-JChen, and M. K. Khan, A blind reversible method for watermarking relational databases based on a time-stamping protocol, Expert Syst. Appl., vol. 39, no. 3, pp. 3185 3196, 212. [2]K. Jawad and A. Khan, Genetic algorithm and difference xpansion based reversible watermarking for relational databases, J. Syst. Softw., vol. 86, no. 11, pp. 2742 2753, 213. 512