Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-04205-8_19
Title: Privacy risk diagnosis: Mining l-diversity
Authors: Zare-Mirakabad, M.-R.
Jantan, A.
Bressan, S. 
Keywords: Apriori algorithm
Knowledge discovery problem
Measuring l-diversity
Monotonicity of l-diversity
Privacy preservation
Issue Date: 2009
Source: Zare-Mirakabad, M.-R.,Jantan, A.,Bressan, S. (2009). Privacy risk diagnosis: Mining l-diversity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5667 LNCS : 216-230. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-04205-8_19
Abstract: Most of the recent efforts addressing the issue of data privacy have focused on devising algorithms for anonymization and diversification. Our objective is upstream of these works: we are concerned with the diagnosis of privacy risk and more specifically in this paper with l-diversity. We show that diagnosing l-diversity for various definitions of the concept is a knowledge discovery problem that can be mapped to the framework proposed by Mannila and Toivonen. The problem can therefore be solved with level-wise algorithms such as the apriori algorithm. We introduce and prove the necessary monotonicity property with respect to subset operator on attributes set for several instantiations of the l-diversity principle. We present and evaluate an algorithm based on the apriori algorithm. This algorithm computes, for instance, "maximum sets of attributes that can safely be published without jeopardizing sensitive attributes", even if they were quasi-identifiers available from external sources, and "minimum subsets of attributes which jeopardize anonymity". © 2009 Springer Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40350
ISBN: 364204204X
ISSN: 03029743
DOI: 10.1007/978-3-642-04205-8_19
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