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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.
This research has been supported by ARC Discovery grants DP0774450 and DP110103142.
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Authors and Affiliations
School of Computer and Information Science, University of South Australia, Mawson Lakes, SA, 5095, Australia
Muzammil M. Baig, Jiuyong Li, Jixue Liu & Xiaofeng Ding
Department of Maths & Computing, University of Southern Queensland, Toowoomba, Queensland, 4350, Australia
Hua Wang
- Muzammil M. Baig
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- Jiuyong Li
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- Jixue Liu
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- Xiaofeng Ding
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- Hua Wang
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Editor information
Editors and Affiliations
School of Computer Science and Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, 151747, Seoul, South Korea
Sang-goo Lee
Computer School, Wuhan University, Luo-jia-shan, Wuchang, 430081, Wuhan, Hubei Province, China
Zhiyong Peng
School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia
Xiaofang Zhou
Department of Computer Science, Kangwon National University, 192-1, Hyoja2-Dong, Chuncheon, 200701, Kangwon, South Korea
Yang-Sae Moon
Institute for Computer Science and Business Information, University of Duisburg-Essen, Schützenbahn 70, 45117, Essen, Germany
Rainer Unland
School of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro, Heungdeok-gu, Cheongju, 4072, Chungbuk, South Korea
Jaesoo Yoo
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Baig, M.M., Li, J., Liu, J., Ding, X., Wang, H. (2012). Data Privacy against Composition Attack. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_24
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