Please use this identifier to cite or link to this item: https://doi.org/10.1145/1538909.1538911
Title: A framework for efficient data anonymization under privacy and accuracy constraints
Authors: Ghinita, G.
Karras, P. 
Kalnis, P. 
Mamoulis, N.
Keywords: Anonymity
Privacy
Issue Date: 2009
Source: Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N. (2009). A framework for efficient data anonymization under privacy and accuracy constraints. ACM Transactions on Database Systems 34 (2). ScholarBank@NUS Repository. https://doi.org/10.1145/1538909.1538911
Abstract: Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and l-diversity. k-anonymity protects against the identification of an individual's record. l-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) l-diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss. (ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem. In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and l-diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss. © 2009 ACM.
Source Title: ACM Transactions on Database Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/39484
ISSN: 03625915
DOI: 10.1145/1538909.1538911
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