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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Zhang, L., Wang, L., Jajodia, S., & Brodsky, A. (2008). Exclusive strategy for generalization algorithms in micro-data disclosure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5094 LNCS, pp. 190–204). https://doi.org/10.1007/978-3-540-70567-3_15
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