Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy preserving frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Protecting business intelligence and customer privacy while outsourcing data mining tasks
Knowledge and Information Systems
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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 Swhich 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 xin 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 xis protected. It is achieved without examining all possible combinations of values of attributes. Our method is driven by classification rules extracted from Sand takes into consideration their confidence and support.