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Data Confidentiality Versus Chase.
- Source :
- Rough Sets, Fuzzy Sets, Data Mining & Granular Computing (9783540725299); 2007, p330-337, 8p
- Publication Year :
- 2007
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540725299
- Database :
- Complementary Index
- Journal :
- Rough Sets, Fuzzy Sets, Data Mining & Granular Computing (9783540725299)
- Publication Type :
- Book
- Accession number :
- 33175805
- Full Text :
- https://doi.org/10.1007/978-3-540-72530-5_39