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Anonymity meets game theory: secure data integration with malicious participants


Anonymity meets game theory: secure data integration with malicious participants

Mohammed, Noman, Fung, Benjamin C.M. and Debbabi, Mourad (2011) Anonymity meets game theory: secure data integration with malicious participants. The VLDB Journal, 20 (4). pp. 567-588. ISSN 1066-8888

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Official URL: http://dx.doi.org/10.1007/s00778-010-0214-6


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 (DkA) 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 DkA framework is not scalable to large data sets. Moreover, DkA 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.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Authors:Mohammed, Noman and Fung, Benjamin C.M. and Debbabi, Mourad
Journal or Publication:The VLDB Journal
Digital Object Identifier (DOI):10.1007/s00778-010-0214-6
Keywords:k-anonymity, Secure data integration, Privacy, Classification
ID Code:36256
Deposited On:22 Dec 2011 21:00
Last Modified:18 Jan 2018 17:36
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