L-Cover: Preserving Diversity by Anonymity

Authors:
Lei Zhang;Lingyu Wang;Sushil Jajodia;Alexander Brodsky
Affiliations:
Center for Secure Information Systems, George Mason University, Fairfax, USA 22030;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada H3G 1M8;Center for Secure Information Systems, George Mason University, Fairfax, USA 22030;Center for Secure Information Systems, George Mason University, Fairfax, USA 22030
Venue:
SDM '09 Proceedings of the 6th VLDB Workshop on Secure Data Management
Year:
2009

Citing 11
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Abstract

To release micro-data tables containing sensitive data, generalization algorithms are usually required for satisfying given privacy properties, such as k -anonymity and l -diversity. It is well accepted that k -anonymity and l -diversity are proposed for different purposes, and the latter is a stronger property than the former. However, this paper uncovers an interesting relationship between these two properties when the generalization algorithms are publicly known. That is, preserving l -diversity in micro-data generalization can be done by preserving a new property, namely, l -cover, which is to satisfy l -anonymity in a special way. The practical impact of this discovery is that it may potentially lead to better heuristic generalization algorithms in terms of efficiency and data utility, that remain safe even when publicized.