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...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, Nikos Mamoulis
• Keywords: Database, VLDB 2007