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2008
Springer

k-Anonymity via Clustering Domain Knowledge for Privacy Preservation

8 years 3 months ago
k-Anonymity via Clustering Domain Knowledge for Privacy Preservation
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key concept in k-anonymity. The tuples whose quasi-identifiers have near effect on the sensitive attributes should be grouped to reduce information loss. The previous investigations ignored this point. This paper studies k-anonymity via clustering domain knowledge. The contributions include: (a) Constructing a weighted matrix based on domain knowledge and proposing measure methods. It carefully considers the effect between the quasi-identifiers and the sensitive attributes. (b) Developing a heuristic algorithm to achieve k-anonymity via clustering domain knowledge based on the measure methods. (c) Implementing the algorithm for privacy preservation, and (d) Experiments on real data demonstrate that the proposed k-anonymous methods decrease 30% information loss compared with basic kanonymity. 1
Taiyong Li, Changjie Tang, Jiang Wu, Qian Luo, She
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where FSKD
Authors Taiyong Li, Changjie Tang, Jiang Wu, Qian Luo, Shengzhi Li, Xun Lin, Jie Zuo
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