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Issue No.12 - December (2010 vol.22)
pp: 1709-1723
Osman Abul , TOBB University of Economics and Technology, Ankara
Francesco Bonchi , Yahoo! Research, Barcelona
Fosca Giannotti , ISTI-CNR, Pisa
The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in [CHECK END OF SENTENCE]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal.
Sequential patterns, spatiotemporal patterns, knowledge hiding, data publishing.
Osman Abul, Francesco Bonchi, Fosca Giannotti, "Hiding Sequential and Spatiotemporal Patterns", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 12, pp. 1709-1723, December 2010, doi:10.1109/TKDE.2009.213
[1] D.E. O'Leary, "Knowledge Discovery as a Threat to Database Security," Knowledge Discovery in Databases, AAAI/MIT Press, 1991.
[2] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. 11th Int'l Conf. Data Eng. (ICDE '95), 2008.
[3] M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., 1979.
[4] R. Srikant and R. Agrawal, "Mining Sequential Patterns: Generalizations and Performance Improvements," Proc. Fifth Int'l Conf. Extending Database Technology (EDBT '96), 2008.
[5] T. Brinkhoff, "Generating Traffic Data," IEEE Data Eng. Bull., vol. 26, no. 2, pp. 19-25, June 2003.
[6] F. Giannotti, M. Nanni, and D. Pedreschi, "Efficient Mining of Temporally Annotated Sequences," Proc. Sixth SIAM Int'l Conf. Data Mining, 2006.
[7] D. Eppstein, "Finding the k Shortest Paths," SIAM J. Computing, vol. 28, no. 2, pp. 652-673, 1997.
[8] E. Frentzos, K. Gratsias, N. Pelekis, and Y. Theodoridis, "Nearest Neighbor Search on Moving Object Trajectories," Proc. Int'l Symp. Large Spatio-Temporal Databases (SSTD '05), 2005.
[9] M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V.S. Verykios, "Disclosure Limitation of Sensitive Rules," Proc. IEEE Knowledge and Data Eng. Exchange Workshop, 1999.
[10] E. Dasseni, V.S. Verykios, A.K. Elmagarmid, and E. Bertino, "Hiding Association Rules by Using Confidence and Support," Proc. Fourth Int'l Workshop Information Hiding, pp. 369-383, 2001.
[11] V.S. Verykios, A.K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni, "Association Rule Hiding," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 4, pp. 434-447, Apr. 2004.
[12] E.D. Pontikakis, A.A. Tsitsonis, and V.S. Verykios, "An Experimental Study of Distortion-Based Techniques for Association Rule Hiding," Proc. 18th Conf. Database Security, 2004.
[13] G. Lee, C.-Y. Chang, and A.L.P. Chen, "Hiding Sensitive Patterns in Association Rules Mining," Proc. 28th Ann. Int'l Computer Software and Applications Conf. (COMPSAC '04), 2004.
[14] Y. Saygin, V.S. Verykios, and C. Clifton, "Using Unknowns to Prevent Discovery of Association Rules," ACM SIGMOD Record, vol. 30, no. 4, pp. 45-54, 2001.
[15] S.R.M. Oliveira and O.R. Zaïane, "Protecting Sensitive Knowledge by Data Sanitization," Proc. Third IEEE Int'l Conf. Data Mining (ICDM '03), pp. 211-218, 2003.
[16] X. Sun and P.S. Yu, "A Border Based Approach for Hiding Sensitive Frequent Itemsets," Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM '05), pp. 426-433, 2005.
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