@book {879,
	title = {Realising the European open science cloud},
	year = {2016},
	abstract = {The European Open Science Cloud (EOSC) aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. This report approaches the EOSC as a federated environment for scientific data sharing and re-use, based on existing and emerging elements in the Member States, with light-weight international guidance and governance, and a large degree of freedom regarding practical implementation.},
	isbn = {978-92-79-61762-1},
	doi = {10.2777/940154},
	url = {http://dx.doi.org/10.2777/940154},
	author = {Ayris, Paul and Berthou, Jean-Yves and Bruce, Rachel and Lindstaedt, Stefanie and Anna Monreale and Mons, Barend and Murayama, Yasuhiro and S{\"o}derg{\r a}rd, Caj and Tochtermann, Klaus and Wilkinson, Ross}
}
@article {990,
	title = {A risk model for privacy in trajectory data},
	journal = {Journal of Trust Management},
	volume = {2},
	number = {1},
	year = {2015},
	pages = {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},
	author = {Anirban Basu and Anna Monreale and Roberto Trasarti and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara}
}
@inbook {636,
	title = {Retrieving Points of Interest from Human Systematic Movements},
	booktitle = {Software Engineering and Formal Methods},
	year = {2014},
	pages = {294{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers{\textquoteright} systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.},
	doi = {10.1007/978-3-319-15201-1_19},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
