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Security and Communication Networks
Volume 2017, Article ID 9532163, 10 pages
Research Article

Practical --Anonymization for Collaborative Data Publishing without Trusted Third Party

1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
2Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
3Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX 78249-0631, USA
4School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
5Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, China, Hangzhou Dianzi University, Hangzhou 310018, China

Correspondence should be addressed to Hong Ding; nc.ude.udh@gnohgnid and Yizhi Ren; moc.liamg@ihziyner

Received 6 October 2016; Revised 9 February 2017; Accepted 27 February 2017; Published 16 March 2017

Academic Editor: Willy Susilo

Copyright © 2017 Jingyu Hua et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In collaborative data publishing (CDP), an -adversary attack refers to a scenario where up to malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed -anonymization scheme, --anonymization, designed to defend against -adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset.