M-invariance: Towards privacy preserving re-publication of dynamic datasets
Refereed conference paper presented and published in conference proceedings

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AbstractThe 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. Copyright 2007 ACM.
All Author(s) ListXiao X., Tao Y.
Name of ConferenceSIGMOD 2007: ACM SIGMOD International Conference on Management of Data
Start Date of Conference12/06/2007
End Date of Conference14/06/2007
Place of ConferenceBeijing
Country/Region of ConferenceChina
Detailed descriptionACM
Pages689 - 700
LanguagesEnglish-United Kingdom
KeywordsGeneralization, M-invariance, Privacy

Last updated on 2020-21-09 at 02:16