We present a generalization of a strategy, called SCIKD, proposed in [7] that allows to reduce a disclosure risk of confidential data in an information system S [10] using methods based on knowledge discovery. The method proposed in [7] protects confidential data against Rule-based Chase, the null value imputation algorithm driven by certain rules [2], [4]. This method identifies a minimal subset of additional data in S which needs to be hidden to guarantee that the confidential data are not revealed by Chase. In this paper we propose a bottom-up strategy which identifies, for each object x in S, a maximal set of values of attributes which do not have to be hidden and still the information associated with secure attribute values of x is protected. It is achieved without examining all possible combinations of values of attributes. Our method is driven by classification rules extracted from S and takes into consideration their confidence and support. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Raś, Z. W., Gürdal, O., Im, S., & Tzacheva, A. (2007). Data confidentiality versus chase. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4482 LNAI, pp. 330–337). Springer Verlag. https://doi.org/10.1007/978-3-540-72530-5_39
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