M-invariance: towards privacy preserving re-publication of dynamic datasets

11 years 4 months ago
M-invariance: towards privacy preserving re-publication of dynamic datasets
The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-publication of the microdata, after it has been updated with insertions and deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously. This paper remedies the drawback. First, we reveal the characteristics of the re-publication problem that invalidate the conventional approaches leveraging k-anonymity and l-diversity. Based on rigorous theoretical analysis, we develop a new generalization principle m-invariance that effectively limits the risk of privacy disclosure in re-publication. We accompany the principle with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Our theoretical results are confirmed by extensive experiments with real data. C...
Xiaokui Xiao, Yufei Tao
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2007
Authors Xiaokui Xiao, Yufei Tao
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