k-anonymised reducts Online publication date: Fri, 29-Aug-2014
by Lior Rokach; Alon Schclar
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 2, No. 3, 2012
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.
Online publication date: Fri, 29-Aug-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com