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Utility-driven anonymization in data publishing

Published: 24 October 2011 Publication History

Abstract

Privacy-preserving data publication has been studied intensely in the past years. Still, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of patterns, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified patterns in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy.

References

[1]
R. Agrawal and R. Srikant. Privacy-preserving data mining. SIGMOD Rec., 29(2):439--450, 2000.
[2]
J. Cao, P. Karras, P. Kalnis, and K.-L. Tan. SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness. The VLDB Journal, 20(1):59--81, 2011.
[3]
K. Chen, G. Sun, and L. Liu. Towards attack-resilient geometric data perturbation. In SDM, 2007.
[4]
A. V. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, 2003.
[5]
G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis. A framework for efficient data anonymization under privacy and accuracy constraints. ACM TODS, 34(2):1--47, 2009.
[6]
M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially private histograms through consistency. PVLDB, 3(1), 2010.
[7]
K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In ICDE, 2006.
[8]
N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, 2007.
[9]
N. Li, T. Li, and S. Venkatasubramanian. Closeness: A new privacy measure for data publishing. IEEE TKDE, 22(7):943--956, 2010.
[10]
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. $\ell$-diversity: Privacy beyond k-anonymity. In ICDE, 2006.
[11]
S. Mukherjee, Z. Chen, and A. Gangopadhyay. A privacy-preserving technique for euclidean distance-based mining algorithms using fourier-related transforms. The VLDB Journal, 15(4):293--315, 2006.
[12]
P. Samarati. Protecting respondents' identities in microdata release. IEEE TKDE, 13(6):1010--1027, 2001.
[13]
R. L. Smith. Efficient monte carlo procedures for generating points uniformly distributed over bounded regions. Operations Research, 32(6):1296--1308, 1984.
[14]
Y. Tao, X. Xiao, J. Li, and D. Zhang. On anti-corruption privacy preserving publication. In ICDE, pages 725--734, 2008.

Cited By

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  • (2015)On Data Publishing with Clustering PreservationACM Transactions on Knowledge Discovery from Data10.1145/27004039:3(1-30)Online publication date: 1-Apr-2015
  • (2013)On the Complexity of t-Closeness Anonymization and Related ProblemsDatabase Systems for Advanced Applications10.1007/978-3-642-37487-6_26(331-345)Online publication date: 2013
  • (2012)Discretionary social network data revelation with a user-centric utility guaranteeProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398475(1572-1576)Online publication date: 29-Oct-2012

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cover image ACM Conferences
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management
October 2011
2712 pages
ISBN:9781450307178
DOI:10.1145/2063576
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 October 2011

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Author Tags

  1. anonymization
  2. pattern-preserving
  3. privacy
  4. utility-driven

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Cited By

View all
  • (2015)On Data Publishing with Clustering PreservationACM Transactions on Knowledge Discovery from Data10.1145/27004039:3(1-30)Online publication date: 1-Apr-2015
  • (2013)On the Complexity of t-Closeness Anonymization and Related ProblemsDatabase Systems for Advanced Applications10.1007/978-3-642-37487-6_26(331-345)Online publication date: 2013
  • (2012)Discretionary social network data revelation with a user-centric utility guaranteeProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398475(1572-1576)Online publication date: 29-Oct-2012

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