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Trust Management VIII 8th IFIP WG 11.11 International Conference, IFIPTM 2014 Singapore, July 7-10, 2014 |
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Conference papers
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.
Cited literature [28 references]
https://hal.inria.fr/hal-01381683
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Submitted on : Friday, October 14, 2016 - 3:20:22 PM
Last modification on : Tuesday, February 26, 2019 - 10:54:02 AM
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, October 14, 2016 - 3:20:22 PM
Last modification on : Tuesday, February 26, 2019 - 10:54:02 AM
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