Privacy, Space and Time: a Survey on Privacy-Preserving Continuous Data Publishing

Abstract

Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis. The manipulation of such data is useful in numerous application domains, e.g., healthcare, intelligent buildings, and traffic monitoring, to name a few. A high percentage of these data carry information of users' activities and other personal details, and thus their manipulation and sharing arise concerns about the privacy of the individuals involved. To enable the secure—from the users' privacy perspective—data sharing, researchers have already proposed various seminal techniques for the protection of users' privacy. However, the continuous fashion in which data are generated nowadays, and the high availability of external sources of information, pose more threats and add extra challenges to the problem. In this survey, we visit the works done on data privacy for continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data.

Keywords

• privacy-preserving algorithms
• continuous data publishing
• location privacy
• microdata privacy
• statistical data privacy