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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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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