Privacy-aware location data publishing
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- Haibo Hu
- Hong Kong Baptist University, Kowloon, Hong Kong
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- Jianliang Xu
- Hong Kong Baptist University, Kowloon, Hong Kong
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- Sai Tung On
- Hong Kong Baptist University, Kowloon, Hong Kong
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- Jing Du
- Hong Kong Baptist University, Kowloon, Hong Kong
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- Joseph Kee-Yin Ng
- Hong Kong Baptist University, Kowloon, Hong Kong
Abstract
<jats:p> This article examines a new problem of <jats:italic>k</jats:italic> -anonymity with respect to a reference dataset in privacy-aware location data publishing: given a user dataset and a sensitive event dataset, we want to generalize the user dataset such that by joining it with the event dataset through location, each event is covered by at least <jats:italic>k</jats:italic> users. Existing <jats:italic>k</jats:italic> -anonymity algorithms generalize every <jats:italic>k</jats:italic> user locations to the same vague value, regardless of the events. Therefore, they tend to overprotect against the privacy compromise and make the published data less useful. In this article, we propose a new generalization paradigm called <jats:italic>local enlargement</jats:italic> , as opposed to conventional hierarchy- or partition-based generalization. Local enlargement guarantees that user locations are enlarged just enough to cover all events <jats:italic>k</jats:italic> times, and thus maximize the usefulness of the published data. We develop an <jats:italic>O</jats:italic> ( <jats:italic>H</jats:italic> <jats:sub> <jats:italic>n</jats:italic> </jats:sub> )-approximate algorithm under the local enlargement paradigm, where <jats:italic>n</jats:italic> is the maximum number of events a user could possibly cover and <jats:italic>H</jats:italic> <jats:sub> <jats:italic>n</jats:italic> </jats:sub> is the Harmonic number of <jats:italic>n</jats:italic> . With strong pruning techniques and mathematical analysis, we show that it runs efficiently and that the generalized user locations are up to several orders of magnitude smaller than those by the existing algorithms. In addition, it is robust enough to protect against various privacy attacks. </jats:p>
Journal
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- ACM Transactions on Database Systems
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ACM Transactions on Database Systems 35 (3), 1-42, 2010-07
Association for Computing Machinery (ACM)
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Keywords
Details 詳細情報について
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- CRID
- 1361418518991885184
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- ISSN
- 15574644
- 03625915
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- Data Source
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- Crossref