Abstract
Some of the knowledge discovered by data mining may contain sensitive information, which should be hidden before sharing the result of data mining. In this paper, we consider that the knowledge for sharing is discovered by frequent pattern mining, and some of the frequent patterns are private, which cannot be shared. Our problem of privacy-preserving frequent pattern sharing is to hide these private patterns before sharing the result of frequent pattern mining, and at the same time maximize the number of non-private frequent patterns to be shared. We show that this problem is NP-hard, and present three item-based pattern sanitization algorithms for transforming the result of frequent pattern mining into a privacy-free frequent pattern set.
This research was supported by the Shanghai Rising-Star Program (No. 05QMX1405), the National Natural Science Foundation of China (No. 60303008), the National Grand Fundamental Research 973 Program of China (No. 2005CB321905).
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References
Calders, T.: Computational complexity of itemset frequency satisfiability. In: PODS, Paris, France, June 2004, pp. 143–154 (2004)
Mielikainen, T.: On inverse frequent set mining. In: Workshop on Privacy Preserving Data Mining, November 2003, pp. 18–23 (2003)
Clifton, C., Marks, D.: Security and privacy implications of data mining. In: ACM SIGMOD Workshop on Data Mining and Knowledge Discovery, Montreal, Canada, pp. 15–19. ACM Press, New York (1996)
Han, J., Kamber, M.: Data Mining: Concept and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)
Oliveira, S.R.M., Zaïane, O.R., Saygın, Y.: Secure Association Rule Sharing. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 74–85. Springer, Heidelberg (2004)
Wang, Z., Wang, W., Shi, B., Boey, S.H.: Preserving private knowledge in frequent pattern mining. In: IEEE ICDM Workshop on Privacy Aspects of Data Mining, Hong Kong, China, pp. 530–534. IEEE Computer Society Press, Los Alamitos (2006)
Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: k-Anonymous Patterns. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 10–21. Springer, Heidelberg (2005)
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure limitation of sensitive rules. In: IEEE Knowledge and Data Engineering Exchange Workshop, Chicago, IL, pp. 45–52. IEEE Computer Society Press, Los Alamitos (1999)
Oliveira, S.R.M., Zaïane, O.R.: Algorithms for balancing privacy and knowledge discovery in association rule mining. In: IDEAS, Hong Kong, China, pp. 54–65 (2003)
Saygin, Y., Verykios, V.S., Clifton, C.: Using unknowns to prevent discovery of association rules. SIGMOD Record 30(4), 45–54 (2001)
Wang, Z., Liu, B., Wang, W., Zhou, H., Shi, B.-L.: An Effective Approach for Hiding Sensitive Knowledge in Data Publishing. In: Yu, J.X., Kitsuregawa, M., Leong, H.-V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 146–157. Springer, Heidelberg (2006)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB, Santiago, Chile, pp. 487–499 (1994)
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Wang, Z., Wang, W., Shi, B., Boey, S.H. (2007). Privacy-Preserving Frequent Pattern Sharing. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_21
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DOI: https://doi.org/10.1007/978-3-540-71703-4_21
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