A Privacy Risk Model for Trajectory Data - Trust Management VIII (IFIPTM 2014) Access content directly
Conference Papers Year : 2014

A Privacy Risk Model for Trajectory Data


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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Dates and versions

hal-01381683 , version 1 (14-10-2016)





Anirban Basu, Anna Monreale, Juan Camilo 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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