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VLDB
2007
ACM

Fast Data Anonymization with Low Information Loss

9 years 11 months ago
Fast Data Anonymization with Low Information Loss
Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy preserving paradigms of k-anonymity and -diversity. k-anonymity protects against the identification of an individual's record. -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) The information loss metrics are counter-intuitive and fail to capture data inaccuracies inflicted for the sake of privacy. (ii) -diversity is solved by techniques developed for the simpler k-anonymity problem, which introduces unnecessary inaccuracies. (iii) The anonymization process is inefficient in terms of computation and I/O cost. In this paper we propose a framework for efficient privacy preservation that addresses these deficiencies. First, we focus on one-dimensional (i.e., single attribute) quasiidentifiers, and study the prope...
Gabriel Ghinita, Panagiotis Karras, Panos Kalnis,
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2007
Where VLDB
Authors Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, Nikos Mamoulis
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