Exclusive Strategy for Generalization Algorithms in Micro-data Disclosure

Authors:
Lei Zhang;Lingyu Wang;Sushil Jajodia;Alexander Brodsky
Affiliations:
Center for Secure Information Systems, George Mason University, Fairfax, USA VA 22030;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada QC H3G 1M8;Center for Secure Information Systems, George Mason University, Fairfax, USA VA 22030;Center for Secure Information Systems, George Mason University, Fairfax, USA VA 22030
Venue:
Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security
Year:
2008

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Abstract

When generalization algorithms are known to the public, an adversary can obtain a more precise estimation of the secret table than what can be deduced from the disclosed generalization result. Therefore, whether a generalization algorithm can satisfy a privacy property should be judged based on such an estimation. In this paper, we show that the computation of the estimation is inherently a recursive process that exhibits a high complexity when generalization algorithms take a straightforward inclusive strategy. To facilitate the design of more efficient generalization algorithms, we suggest an alternative exclusive strategy, which adopts a seemingly drastic approach to eliminate the need for recursion. Surprisingly, the data utility of the two strategies are actually not comparable and the exclusive strategy can provide better data utility in certain cases.