01825nas a2200217   4500008004100000245004800041210004600089300000600135490000600141520119400147100001801341700001901359700002201378700002401400700002101424700002001445700002301465700001901488700002401507856007601531       2015                        eng d00aA risk model for privacy in trajectory data0 arisk model for privacy in trajectory data  a90 v23 aTime 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.1 aBasu, Anirban1 aMonreale, Anna1 aTrasarti, Roberto1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi  uhttps://kdd.isti.cnr.it/publications/risk-model-privacy-trajectory-data01965nas a2200193   4500008004100000245007600041210006900117260000900186520136400195100001801559700002401577700001901601700002001620700002101640700002301661700002001684700001901704856004801723       2014                        eng d00aCF-inspired Privacy-Preserving Prediction of Next Location in the Cloud0 aCFinspired PrivacyPreserving Prediction of Next Location in the   bIEEE3 aMobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. 1 aBasu, Anirban1 aCorena, Juan Camilo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aKiyomoto, Shinsaku1 aVaidya, Jaideep1 aMiyake, Yutaka  uhttp://dx.doi.org/10.1109/CloudCom.2014.11401645nas a2200205   4500008003900000245004500039210004300084300001400127520105800141100001801199700001901217700002401236700002101260700002001281700002301301700001901324700002401343700002201367856005001389       2014                          d00aA Privacy Risk Model for Trajectory Data0 aPrivacy Risk Model for Trajectory Data  a125–1403 aTime 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.1 aBasu, Anirban1 aMonreale, Anna1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-662-43813-8_9