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|Title:||A framework for efficient data anonymization under privacy and accuracy constraints|
|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|
|Appears in Collections:||Staff Publications|
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