TY - JOUR T1 - A risk model for privacy in trajectory data JF - Journal of Trust Management Y1 - 2015 A1 - Anirban Basu A1 - Anna Monreale A1 - Roberto Trasarti A1 - Juan Camilo Corena A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Shinsaku Kiyomoto A1 - Yutaka Miyake A1 - Tadashi Yanagihara AB - 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. VL - 2 ER -