Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
MoveSafe: a framework for transportation mode-based targeted alerting in disaster response
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
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This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set.