Full Citation

Title: Cloning for Privacy Protection in Multiple Independent Data Publications

Citation Type: Conference Paper

Publication Year: 2011

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Abstract: Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed toanonymize one dataset and a series of datasets of a data owner. However, no method has been proposed for the anonymization of data of multiple independent data publications. A data owner publishes a dataset, which contains overlapping population with other datasets published by other independent data owners. In this paper we analyze the privacy risk in the such scenario and vulnerability of partitioned based anonymization methods. We show that no partitioned based anonymization methods can protect privacy in arbitrary data distributions, and identify a case that the privacy can be protected in the scenario. We propose a new generalizationprinciple $-cloning to protect privacy for multiple independent data publications. We also develop an effective algorithm to achieve the$-cloning. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves good data utility.

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Authors: Baig, Muzammil M.; Liu, Jixue; Wang, Hua; Li, Jiuyong

Conference Name: ACM Conference on Information and Knowledge Management (CIKM)

Publisher Location: Glasgow, Scotland

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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