Full Citation

Title: m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets

Citation Type: Miscellaneous

Publication Year: 2007

ISBN: 9781595936868

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Abstract: 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 mi-crodata. Our theoretical results are confirmed by extensive experiments with real data.

Url: http://www.utdallas.edu/~muratk/courses/privacy08f_files/m-invariance.pdf

User Submitted?: No

Authors: Xiao, Xiaokui; Tao, Yufei

Publisher: Chinese University of Hong Kong

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States