期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2008
卷号：8
期号：1
页码：115-121
出版社：International Journal of Computer Science and Network Security
摘要：Data privacy is the most acclaimed problem when publishing individual data. It ensures individual data publishing without disclosing sensitive data. The much popular approach, is K-Anonymity, where data is transformed to equivalence classes, each class having a set of K- records that are indistinguishable from each other. But several authors have pointed out numerous problems with K-anonymity and have proposed techniques to counter them or avoid them. l-diversity and t-closeness are such techniques to name a few. Our study has shown that all these techniques increase computational effort to practically infeasible levels, though they increase privacy. A few techniques account for too much of information loss, while achieving privacy. In this paper, we propose a novel, holistic approach for achieving maximum privacy with no information loss and minimum overheads (as only the necessary tuples are transformed). We address the data privacy problem using fuzzy set approach, a total paradigm shift and a new perspective of looking at privacy problem in data publishing. Our practically feasible method in addition, allows personalized privacy preservation, and is useful for both numerical and categorical attributes.
关键词：Privacy preserving; data privacy; fuzzy information; anonymity
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