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Instant Anonymization MEHMET ERCAN NERGIZ, Zirve University ACAR TAMERSOY and YUCEL SAYGIN, Sabanci University Anonymization-based privacy protection ensures that data cannot be traced back to individuals. Researchers working in this area have proposed a wide variety of anonymization algorithms, many of which require a considerable number of database accesses. This is a problem of ef ciency, especially when the released data is subject to visualization or when the algorithm needs to be run many times to get an acceptable ratio of privacy/utility. In this paper, we present two instant anonymization algorithms for the privacy metrics k-anonymity and -diversity. Proposed algorithms minimize the number of data accesses by utilizing the summary structure already maintained by the database management system for query selectivity. Experiments on real data sets show that in most cases our algorithm produces an optimal anonymization, and it requires a single scan of data as opposed to hundreds of scans required by the state-of-the-art algorithms. Categories and Subject Descriptors: H.2.8 [Database Applications]: Statistical Databases; K.4.1 [Public Policy Issues]: Privacy General Terms: Algorithms, Security, Legal Aspects Additional Key Words and Phrases: k-anonymity, ell-diversity, privacy, algorithms ACM Reference Format: Nergiz, M. E., Tamersoy, A., and Saygin, Y. 2011. Instant
ACM Transactions on Database Systems (TODS) – Association for Computing Machinery
Published: Mar 1, 2011
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