Privacy protection of data with safe watermark extraction using data hiding and sparse sampling

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-35 Number-3
Year of Publication : 2016
Authors : H.N.RAnotkar, M.S.Deshmukh
  10.14445/22312803/IJCTT-V35P130

MLA

H.N.RAnotkar, M.S.Deshmukh "Privacy protection of data with safe watermark extraction using data hiding and sparse sampling". International Journal of Computer Trends and Technology (IJCTT) V35(3):157-163, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Data privacy protection is a very important aspect in this cloud computing era. Also,it becomes very easy for the user to collect the data from the various sources without worrying about the copyright information as a result of the rapid growth of the Internet and social networks. Hence adding certain ownership information into the data(image) becoming necessary for the data owners now-a-days. To do so watermarking technique is used. This restricts the reuse and republishing of the data to authenticated users only. In this paper, we propose a system that enhances privacy protection of image data as well as the safe watermark extraction in a simultaneous manner by using sparse sampling, data hiding and secure computation. We provide enhancement in the privacy of the image data by applying layer of encryption over the data using data hiding technique in which we add identity bits to the image data before it is passed to the sparse sampling transformation. This will make the proposed architecture tolerable against the semihonest security assumption required for the simultaneous operation in sparse sampling domain and without these bits an attacker could not get the original data. In SS transformation we deal with DWT coefficients of image.

References
[1] Rafael C. Gonzalez, Richard E. Woods.(1992), Digital Image Processing(2nd edition), NJ:Prentice Hall
[2] P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in Proc. Adv. Cryptology- Eurocrypt, 1999, pp. 223–238.
[3] J. Eggers, J. Su, and B. Girod, “Public key watermarking by eigenvectors of linear transforms,” in Proc. Euro. Signal Process. Conf., 2000.
[4] A. Adelsbach and A. Sadeghi, “Zero-knowledge watermark detection and proof of ownership,” in Proc. 4th Int. Workshop Inf. Hiding,vol. 2137. 2001, pp. 273–288.
[5] B. Goethals, S. Laur, H. Lipmaa, and T. Mielikainen, “On private scalar product computation for privacy-preserving data-mining,” in Proc. 7th Int. Conf. Inf. Security Cryptology, 2004, pp. 104–120.
[6] O. Goldreich, The Foundations of Cryptography. Cambridge, U.K.:Cambridge Univ. Press, 2004.
[7] M. Malkin and T. Kalker, “A cryptographic method for secure watermark detection,” in Proc. 8th Int. Workshop Inf. Hiding, 2006, pp. 26–41.
[8] K. Liu, H. Kargupta, and J. Ryan, “Random projectionbased multiplicative data perturbation for privacy preserving distributed data mining,”IEEE Trans. Knowl. Data Eng., vol. 18, no. 1, pp. 92–106, Jan. 2006.
[9] D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52,no. 4, pp. 1289–1306, Apr.2006.
[10] M. Rudelson and R. Vershynin, “Sparse reconstructions by convex relaxation: Fourier and Gaussian measurements,” in Proc. Conf. Inf. Sci. Syst., Mar. 2006, pp. 207–212.
[11] Z. Erkin, A. Piva, S. Katzenbeisser, R. Lagendijk, J. Shokrollhi,G. Neven, et al., “Protection and retrieval of encrypted multimedia content: When cryptography meets signal processing,” EURASIP J. Inf. Security, vol. 7, no. 2, pp. 1–20, 2007
[12] J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 5, no. 12, pp.4655-4666. Dec. 2007
[13] A. Orsdemir, H. O. Altun, G. Sharma, and M. F. Bocko, “On the security and robustness of encryption via compressed sensing,” in Proc. IEEE Military Commun. Conf., Nov. 2008, pp. 1040–1046
[14] D. Hsu, S. M. Kakade, J. Langford, and T. Zhang, “Multilabel prediction via compressed sensing,” in Proc. NIPS, 2009, pp. 772–780
[15] W. Lu, A. L. Varna, and M.Wu, “Security analysis for privacy preserving search for multimedia,” in Proc. IEEE 17th Int. Conf. Image Process.,Sep. 2010, pp. 2093–2096.
[16] M. Davenport, P. Boufounos, M. Wakin, and R. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Topics SignalProcess., vol. 4, no. 2, pp. 445–460, Apr. 2010.
[17] Anilkumar Katharotiya ,Swati Patel, Mahesh Goyani, “Comparative Analysis between DCT & DWT Techniques of Image compression” in Journal of Information Engineering and Applications Vol 1, No.2, 2011
[18] T. Bianchi and A. Piva, “Secure watermarking for multimedia content protection: A review of its benefits and open issues,” IEEE Signal Process. Mag., vol. 30, no. 2, pp. 87–96, Mar. 2013.
[19] Qia Wang, Wenjun Zeng, Fellow, IEEE, and Jun Tian,Member “A compressive sensing based secure watermark detection and privacy preserving storage framework” in, IEEE issues in IEEE Transactions on image processing, vol. 23, no. 3,march 2014.
[20] Tsung-Yuan Liu and Wen-Hsiang Tsai “Generic Lossless Visible Watermarking-A New Approach” in IEEE transactions on image processing, vol. 19, no. 5, may 2010.

Keywords
Watermark Embedding, Watermark extraction, data hiding sparse sampling, secure computation, DWT.