A risk model for privacy in trajectory data
| Title | A risk model for privacy in trajectory data |
| Publication Type | Journal Article |
| Year of Publication | 2015 |
| Authors | Basu, A, Monreale, A, Trasarti, R, Corena, JC, Giannotti, F, Pedreschi, D, Kiyomoto, S, Miyake, Y, Yanagihara, T |
| Journal | Journal of Trust Management |
| Volume | 2 |
| Issue | 1 |
| Pagination | 9 |
| 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 and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements. |
| DOI | 10.1186/s40493-015-0020-6 |
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