Letizia Bertolaja

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—The aim of proximity services is to raise alerts based on the distance between moving objects. While distance can be easily computed from the objects' geographical locations, privacy concerns in revealing these locations exist, especially when proximity among users is being computed. Distance preserving transformations have been proposed to solve this(More)
Spatial and temporal generalization emerged in the literature as a common approach to preserve location privacy. However, existing solutions have two main shortcomings. First, spatio-temporal generalization can be used with different objectives: for example, to guarantee anonymity or to decrease the sensitivity of the location information. Hence, the(More)
Proximity services alert users about the presence of other users or moving objects based on their distance. Distance preserving transformations are among the techniques that may be used to avoid revealing the actual position of users while still effectively providing these services. Some of the proposed transformations have been shown to actually guarantee(More)
—Many solutions proposed in the literature to enforce privacy in presence of location information use, implicitly or explicitly , spatial granularities. However, most of the contributions do not describe the formal and computational properties of this tool in details. In this paper we propose three families of spatial granularities, specifically designed(More)
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