Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Instant anonymization

Instant anonymization 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Database Systems (TODS) Association for Computing Machinery

Loading next page...
 
/lp/association-for-computing-machinery/instant-anonymization-6B80sT2hv8

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
0362-5915
DOI
10.1145/1929934.1929936
Publisher site
See Article on Publisher Site

Abstract

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

Journal

ACM Transactions on Database Systems (TODS)Association for Computing Machinery

Published: Mar 1, 2011

There are no references for this article.