We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Zhang, N. (2007). Towards comprehensive privacy protection in data clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4426 LNAI, pp. 1096–1104). Springer Verlag. https://doi.org/10.1007/978-3-540-71701-0_124
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