Anonymity meets game theory: secure data integration with malicious participants

Anonymity meets game theory: secure data integration with malicious participants Data integration methods enable different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In VLDBJ 2006, Jiang and Clifton (Very Large Data Bases J (VLDBJ) 15(4):316–333, 2006 ) propose a secure Distributed k -Anonymity (D k A) framework for integrating two private data tables to a k -anonymous table in which each private table is a vertical partition on the same set of records. Their proposed D k A framework is not scalable to large data sets. Moreover, D k A is limited to a two-party scenario and the parties are assumed to be semi-honest. In this paper, we propose two algorithms to securely integrate private data from multiple parties (data providers). Our first algorithm achieves the k -anonymity privacy model in a semi-honest adversary model. Our second algorithm employs a game-theoretic approach to thwart malicious participants and to ensure fair and honest participation of multiple data providers in the data integration process. Moreover, we study and resolve a real-life privacy problem in data sharing for the financial industry in Sweden. Experiments on the real-life data demonstrate that our proposed algorithms can effectively retain the essential information in anonymous data for data analysis and are scalable for anonymizing large data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Anonymity meets game theory: secure data integration with malicious participants

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-010-0214-6
Publisher site
See Article on Publisher Site

Abstract

Data integration methods enable different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In VLDBJ 2006, Jiang and Clifton (Very Large Data Bases J (VLDBJ) 15(4):316–333, 2006 ) propose a secure Distributed k -Anonymity (D k A) framework for integrating two private data tables to a k -anonymous table in which each private table is a vertical partition on the same set of records. Their proposed D k A framework is not scalable to large data sets. Moreover, D k A is limited to a two-party scenario and the parties are assumed to be semi-honest. In this paper, we propose two algorithms to securely integrate private data from multiple parties (data providers). Our first algorithm achieves the k -anonymity privacy model in a semi-honest adversary model. Our second algorithm employs a game-theoretic approach to thwart malicious participants and to ensure fair and honest participation of multiple data providers in the data integration process. Moreover, we study and resolve a real-life privacy problem in data sharing for the financial industry in Sweden. Experiments on the real-life data demonstrate that our proposed algorithms can effectively retain the essential information in anonymous data for data analysis and are scalable for anonymizing large data sets.

Journal

The VLDB JournalSpringer Journals

Published: Aug 1, 2011

References

  • Security control methods for statistical databases
    Adam, N.R.; Wortman, J.C.

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