Abstract
Privacy is one of major concerns when data containing sensitive information needs to be released for ad hoc analysis, which has attracted wide research interest on privacy-preserving data publishing in the past few years. One approach of strategy to anonymize data is generalization. In a typical generalization approach, tuples in a table was first divided into many QI (quasi-identifier)-groups such that the size of each QI-group is no less than k. Clustering is to partition the tuples into many clusters such that the points within a cluster are more similar to each other than points in different clusters. The two methods share a common feature: distribute the tuples into many small groups. Motivated by this observation, we propose a clustering-based k-anonymity algorithm, which achieves k-anonymity through clustering. Extensive experiments on real data sets are also conducted, showing that the utility has been improved by our approach.
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He, X., Chen, H., Chen, Y., Dong, Y., Wang, P., Huang, Z. (2012). Clustering-Based k-Anonymity. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_34
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DOI: https://doi.org/10.1007/978-3-642-30217-6_34
Publisher Name: Springer, Berlin, Heidelberg
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