Title: k-anonymised reducts
Authors: Lior Rokach; Alon Schclar
Department of Information Systems Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva, 84105, Israel.
School of Computer Science, Academic College of Tel-Aviv Yafo, P.O. Box 8401, Tel-Aviv, 61083, Israel; Deutsche Telekom Research Laboratories, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
Abstract: Privacy-preserving data mining aims to prevent the exposure of sensitive information as a result of mining algorithms. This is commonly achieved by data anonymisation. One way to anonymise data is by adherence to the k-anonymity concept which requires that the probability to identify an individual by linking databases does not exceed 1/k. In this paper, we propose an algorithm which utilises rough set theory to achieve k-anonymity. The basic idea is to partition the original dataset into several disjoint reducts such that each one of them adheres to k-anonymity. We show that it is easier to make each reduct comply with k-anonymity if it does not contain all quasi-identifier attributes. Moreover, our procedure ensures that even if the attacker attempts to rejoin the reducts, the k-anonymity is still preserved. Unlike other algorithms that achieve k-anonymity, the proposed method requires no prior knowledge of the domain hierarchy taxonomy.
Keywords: k-anonimity; rough set theory; reducts; privacy preservation; data mining; privacy protection; data anonymisation; data security.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2012 Vol.2, No.3, pp.196 - 210
Date of acceptance: 23 Sep 2011
Available online: 24 May 2012