Abstract
When rules are induced, some rules can be supported only by a very small number of objects. Such rules often correspond to special cases so that supporting objects may be easily estimated. If the rules with small support include some sensitive data, this estimation of objects is not very good in the sense of data privacy. Considering this fact, we investigate utilization of imprecise rules for privacy protection in rule induction. Imprecise rules are rules classifying objects only into a set of possible classes. Utilizing imprecise rules, we propose an algorithm to induce k-anonymous rules, rules with k or more supporting objects. We demonstrate that the accuracy of the classifier with rules induced by the proposed algorithm is not worse than that of the classifier with rules induced by the conventional method. Moreover, the advantage of the proposed method with imprecise rules is examined by comparing other conceivable method with precise rules.
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This work was partially supported by JSPS KAKENHI Grant Number 26350423.
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Inuiguchi, M., Hamakawa, T., Ubukata, S. (2015). Imprecise Rules for Data Privacy. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_12
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DOI: https://doi.org/10.1007/978-3-319-25754-9_12
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