Research Article
Watermarking Shape Datasets with Utility and Distance Preservation
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 133 - Issue 16 |
| Published: January 2016 |
| Authors: Anshika .V. Gupta, B. M. Patil, V. M. Chandode |
10.5120/ijca2016908092
|
Anshika .V. Gupta, B. M. Patil, V. M. Chandode . Watermarking Shape Datasets with Utility and Distance Preservation. International Journal of Computer Applications. 133, 16 (January 2016), 4-9. DOI=10.5120/ijca2016908092
@article{ 10.5120/ijca2016908092,
author = { Anshika .V. Gupta,B. M. Patil,V. M. Chandode },
title = { Watermarking Shape Datasets with Utility and Distance Preservation },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 133 },
number = { 16 },
pages = { 4-9 },
doi = { 10.5120/ijca2016908092 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Anshika .V. Gupta
%A B. M. Patil
%A V. M. Chandode
%T Watermarking Shape Datasets with Utility and Distance Preservation%T
%J International Journal of Computer Applications
%V 133
%N 16
%P 4-9
%R 10.5120/ijca2016908092
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Due to promulgation of data over internet significance of protection of one’s intellectual property is the important topic with technological and legal aspects. Watermarking scheme is used for establishing the ownership of dataset containing multiple objects. As watermarking scheme distorts distance relationship graph, methodology preserves utility of dataset by preserving important distance properties such as nearest neighbor (NN) and minimum spanning tree (MST) of the original data set. We use fast algorithms for NN and MST which gives improved security without any sacrifice in distance relationships then NN and MST algorithms used earlier.
References
- D.W. Aha, D. Kibler, and M.K. Albert, “Instance based learningalgorithms,” Mach. Learn., vol. 6, no. 1, pp. 37–66, 1991.
- C. G. Atkeson, A. W. Moore, and S. Schaal, “Locally weightedlearning,” Artif. Intell. Rev., vol. 11, pp. 11–73, Feb. 1997.
- S.I.Zoumpoulis, N.M. Vlachos and M.Freris,“Right-protected data publishing with provable distance-based mining” IEEE Transactions on knowledge and data engineering, vol. 26, no.8, aug.2014.
- Y. Xu, V. Olman, and D. Xu, “Minimum spanning treesfor gene expression data clustering,” Genome Inform., vol. 12,pp. 24–33, 2001.
- J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometricframework for nonlinear dimensionality reduction,” Sci.,vol. 290, no. 5500, pp. 2319–2323, 2000.
- M. Vlachos, C. Lucchese, D. Rajan, and P. S. Yu, “Ownership protectionof shape datasets with geodesic distance preservation,” inProc. 11th Int. Conf. EDBT, Nantes, France, 2008, pp. 276–286.
- I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Securespread spectrum watermarking for multimedia,” IEEE Trans.Image Process., vol. 6, no. 12, pp. 1673–1687, Dec. 1997.
- F. Hartung, J.K. Su, and B. Girod., “Spread spectrum watermarking:Malicious attacks and counterattacks,” in Proc. SPIE SecurityWatermarking Multimedia Contents, vol. 3657, San Jose, CA, USA,1999.
- V. Solachidis and I. Pitas, “Watermarking polygonal lines usingFourier descriptors,” IEEE Comput. Graph. Appl., vol. 24, no. 3,pp. 44–51, May/Jun. 2004.
- R.S. Agrawal and J.A. Kiernan, “Watermarking relational databases,”in Proc. 28th Int. Conf. VLDB, Hong Kong, China, 2002,pp. 155–166.
- R. Sion, M. J. Atallah, and S. Prabhakar, “Rights protection fordiscrete numeric streams,” IEEE Trans. Knowl. Data Eng., vol. 18,no. 5, pp. 699–714, May 2006.
- H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar, “On theprivacy preserving properties of random data perturbation techniques,”in Proc. 3rd IEEE ICDM, Washington, DC, USA, 2003,pp. 99–106.
- K. Chen and L. Liu, “Privacy preserving data classification withrotation perturbation,” in Proc. 5th ICDM, Washington, DC, USA,2005, pp. 589–592.
- R. Sion, M. Atallah, and S. Prabhakar, “Rights protectionfor relational data,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 12,pp. 1509–1525, Dec. 2004.
- C. C. Aggarwal and P. S. Yu, “A condensation approach to privacypreserving data mining,” in Proc. Int. Conf. EDBT, Crete, Greece,2004, pp. 183–199.
- H. Yu, J. Vaidya, and X. Jiang. Privacy-PreservingSVM Classification on Vertically Partitioned Data. InPAKDD, pages 647–656, 2006.
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