Data privacy against composition attack

Baig, Muzammil M. and Li, Jiuyong and Liu, Jixue and Ding, Xiaofeng and Wang, Hua (2012) Data privacy against composition attack. In: DASFAA 2012: 17th International Conference on Database Systems for Advanced Applications, 15-18 Apr 2012, Busan, South Korea.

Abstract

Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ,α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ,α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to published version due to publisher copyright policy.
Depositing User: Dr Hua Wang
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 05 Oct 2012 07:58
Last Modified: 03 Jul 2013 01:14
Uncontrolled Keywords: composition attack; data anonymity; privacy; population statistics
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0806 Information Systems > 080604 Database Management
08 Information and Computing Sciences > 0804 Data Format > 080402 Data Encryption
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi: 10.1007/978-3-642-29038-1_24
URI: http://eprints.usq.edu.au/id/eprint/21460

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