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An Effective Algorithm for Data Privacy in Distributed Environment Using Wavelets
Abstract
Organizations including census bureau, medical establishments and Insurance companies collect and publish statistical information. Recent researches about the disclosing of individual records shows that £-differetial privacy gurantees higher data utility in the statistical area and provides privacy. Existing methods those capture the data provides little data utility. This paper focuses on privacy preserving data publishing that achieves £-differential privacy through wavelet transform. The experimental studies based on the real and the synthetic datasets.
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References
N.R. Adam and J.C. Worthmann, “Security-Control Methods for Statistical Databases: A Comparative Study,” ACM Computing Surveys, vol. 21, no. 4, pp. 515-556, 1989
B.C.M. Fung, K. Wang, R. Chen, and P.S. Yu, “Privacy-Preserving Data Publishing: A Survey of Recent Developments,” ACM Computing Surveys, vol. 42, no. 4, pp. 14:1-53, 2010.
R.C.-W. Wong, A.W.-C. Fu, K. Wang, and J. Pei, “Minimality Attack in Privacy Preserving Data Publishing,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 543-554, 2007.
S.R. Ganta, S.P. Kasiviswanathan, and A. Smith, “Composition Attacks and Auxiliary Information in Data Privacy,” Proc. 14th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp. 265-273, 2008.
D. Kifer, “Attacks on Privacy and de Finetti’s Theorem,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 127-138, 2009.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating Noise to Sensitivity in Private Data Analysis,” Proc. Third Theory of Cryptography Conf. (TCC), pp. 265-284, 2006.
E.J. Stollnitz, T.D. Derose, and D.H. Salesin, Wavelets for Computer Graphics: Theory and Applications. Morgan Kaufmann Publishers,1996.
K. Chakrabarti, M.N. Garofalakis, R. Rastogi, and K. Shim, “Approximate Query Processing Using Wavelets,” The VLDB J., vol. 10, nos. 2/3, pp. 199-223, 2001.
M.N. Garofalakis and P.B. Gibbons, “Wavelet Synopses with Error Guarantees,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 476-487, 2002.
V. Iyengar, “Transforming Data to Satisfy Privacy Constraints,” Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 279-288, 2002.
G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis, “Fast Data Anonymization with Low Information Loss,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 758-769, 2007.
J. Gray, A. Bosworth, A. Layman, and H. Pirahesh,Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross- Tab, and Sub-Total,” Proc. 12th IEEE Int’l Conf. Data Eng. (ICDE), pp. 152- 159, 1996.
D. Donoho and I. Johnstone, “Ideal Spatial Adaptation via Wavelet Shrinkage,” Biometrika, vol. 81, pp. 425-455, 1994.
M. Elad, “Why Simple Shrinkage is Still Relevant for Redundant Representations?” IEEE Trans. Information Theory, vol. 52, no. 12, pp. 5559-5569, Dec. 2006.
A.Chambolle, R.A. DeVore, N.-Y. Lee, and B.J. Lucier, “Nonlinear Wavelet Image Processing: Variational Problems, Compression, and Noise Removal through Wavelet Shrinkage,” IEEE Trans.Image Processing, vol. 7, no. 3, pp. 319-335, Mar. 1998.
S.G. Chang, B. Yu, and M. Vetterli, “Adaptive Wavelet Thres olding for Image Denoising and Compression,” IEEE Trans. Image Processing, vol. 9, no. 9, pp. 1532- 1546, Sept. 2000.
D. Donoho and I. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” J. Am. Statistical Assoc., vol. 90, pp. 1200-1224, 1995.
S.G. Chang, B. Yu, and M. Vetterli, “Spatially Adaptive Wavelet Thresholding with Context Modeling for Image Denoising,” IEEE Trans. Image Processing, vol. 9, no. 9, pp. 1522-1531, Sept. 2000.
jjhala, D. Kifer, J.M Abowd, J. Gehrke, and L. Vilhuber, “Privacy: Theory Meets Practice on the Map,” Proc. 24th IEEE Int’l Conf. Data Eng. (ICDE), pp. 277-286, 2008.
A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas, “Releasing Search Queries and Clicks Privately,” Proc. Int’l Conf.World Wide Web (WWW), pp. 171-180, 2009.
M. Go¨ tz, A. Machanavajjhala, G. Wang, X. Xiao, and Gehrke, “Publishing Search Logs - A Comparative Study of Privacy Guarantees,” to be published in IEEE Trans. Knowledge and Data Eng.
D.N.A.Asuncion,“UCI machine learning repository,”,2007.[Online].Available:http://www.ics.uci.edu/mlearn/MLRe pository.html
J.S. Vitter and M. Wang, “Approximate Computation of Multi- dimensional Aggregates of Sparse Data Using Wavelets,” Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 193- 204, 1999.
M.N. Garofalakis and A. Kumar, “Wavelet Synopses forGeneral Error Metrics,” ACM Trans. Database Systems, vol. 30, no. 4, pp. 888-928, 2005.
K. Chaudhuri and C. Monteleoni, “Privacy-Preserving Logistic Regression,” Proc. 22nd Ann. Conf. Neural Information Processing Systems (NIPS), pp. 289- 296, 2008.
Blum, K. Ligett, and A. Roth, “A Learning Theory Approach of Non-Interactive Database Privacy,” Proc. 40th Ann. ACM Symp. Theory of Computing (STOC), pp. 609-618, 2008.
S.P. Kasiviswanathan, H.K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, “What Can We Learn Privately?” Proc. 49th Ann. IEEE Symp. Foundations of Computer Science (FOCS), pp. 531-540, 2008.
K. Nissim, S. Raskhodnikova, and A. Smith, “Smooth Sensitivity and Sampling in Private Data Analysis,” Proc. 39th Ann. ACM Symp. Theory of Computing (STOC), pp. 75-84, 2007.
Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and Talwar, “Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release,” Proc. 26th ACM SIGMOD- SIGACT-SIGART Symp. Principles of Database Systems (PODS), pp. 273-282, 2007.
M. Hay, V. Rastogi, G. Miklau, and D. Suciu, “Boosting the Accuracy of Differentially-Private Queries through Consistency,” Proc. VLDB Endowment, vol. 3, no. 1, pp. 1021-1032, 2010.
C. Li, M. Hay, V. Rastogi, G. Miklau, and A. McGregor, “Optimizing Linear Counting Queries under Differential Privacy,” Proc. 29th ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS), pp. 123-134, 2010.
A.Ghosh, T. Roughgarden, and M. Sundararajan, Universally Utility-Maximizing Privacy Mechanisms,” Proc. Ann. ACM Symp. Theory of Computing (STOC), pp. 351-360, 2009.
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