The hardness of (ε, m)-anonymity

Authors:
Yujia Li;Dong Li;Xianmang He;Wei Wang;Huahui Chen
Affiliations:
School of Computer Science and Technology, Fudan University, Shanghai, China;Information Center, National Natural Science Foundation of China, Beijing, China;School of Information Science and Engineering, NingBo University, China;School of Computer Science and Technology, Fudan University, Shanghai, China;School of Information Science and Engineering, NingBo University, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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Abstract

When a table containing individual data is published, disclosure of sensitive information should be prohibitive. (ε, m)-anonymity was a new anonymization principle for preservation of proximity privacy, in publishing numerical sensitive data. It is shown to be NP-Hard to (ε, m)-anonymize a table minimizing the number of suppressed cells. Extensive performance study verified our findings that our algorithm is significantly better than the traditional algorithms presented in the paper[1].