PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Basu A., Monreale A., Corena J. C., Giannotti F., Dino P., Kiyomoto S., Miyake Y., Yanagihara T., Trasarti R. A privacy risk model for trajectory data. In: IFIPTM 2014 - Trust Management VIII. 8th IFIP WG 11.11 International Conference (Singapore, 7-10 July 2014). Proceedings, pp. 125 - 140. Jianying Zhou, Nurit Gal-Oz, Jie Zhang, Ehud Gudes (eds.). (IFIP Advances in Information and Communication Technology, vol. 430). Springer, 2014.
 
 
Abstract
(English)
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.
URL: http://link.springer.com/chapter/10.1007%2F978-3-662-43813-8_9
DOI: 10.1007/978-3-662-43813-8_9
Subject Spatio-temporal data
Privacy
K.4.1 Public Policy Issues. Privacy


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