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A Privacy Risk Model for Trajectory Data

Abstract : Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data.
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Anirban Basu, Anna Monreale, Juan Corena, Fosca Giannotti, Dino Pedreschi, et al.. A Privacy Risk Model for Trajectory Data. 8th IFIP International Conference on Trust Management (IFIPTM), Jul 2014, Singapore, Singapore. pp.125-140, ⟨10.1007/978-3-662-43813-8_9⟩. ⟨hal-01381683⟩

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