Clustering-Based k-anonymity

Authors:
Xianmang He;HuaHui Chen;Yefang Chen;Yihong Dong;Peng Wang;Zhenhua Huang
Affiliations:
School of Information Science and Technology, NingBo University, Ning Bo, P.R. China;School of Information Science and Technology, NingBo University, Ning Bo, P.R. China;School of Information Science and Technology, NingBo University, Ning Bo, P.R. China;School of Information Science and Technology, NingBo University, Ning Bo, P.R. China;School of Computer Science and Technology, Fudan University, Shanghai, P.R. China;School of Electronic and Information Engineering, Tongji University, Shanghai, P.R. China
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Year:
2012

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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.