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This paper investigates privacy issues in Location-Based Services (LBS) under the assumption that the adversary may be able to understand that a sequence of (anonymous) requests have been issued by the same user. A formal framework is presented to model this kind of privacy attack under two different assumptions in terms of the adversary’s knowledge about(More)
A major feature of the emerging geo-social networks is the ability to notify a user when any of his friends (also called buddies) happens to be geographically in proximity. This proximity service is usually offered by the network itself or by a third party service provider (SP) using location data acquired from the users. This paper provides a rigorous(More)
Geo-social networks (GeoSNs) provide context-aware services that help associate location with users and content. The proliferation of GeoSNs indicates that they're rapidly attracting users. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins." However, this ability to reveal users' locations causes(More)
Online social networks often involve very large numbers of users who share very large volumes of content. This content is increasingly being tagged with geo-spatial and temporal coordinates that may then be used in services. For example, a service may retrieve photos taken in a certain region. The resulting geo-aware social networks (GeoSNs) pose privacy(More)
The problem of protecting user’s privacy in Location-Based Services (LBS) has been extensively studied recently and several defense techniques have been proposed. In this contribution, we first present a categorization of privacy attacks and related defenses. Then, we consider the class of defense techniques that aim at providing privacy through anonymity(More)
Proximity based services are location based services (LBS) in which  the service adaptation depends on the comparison between a given threshold value and the distance between a user and other (possibly moving) entities.  While privacy preservation in LBS has lately received much attention, very limited work has been done on(More)
Spatial generalization has been recently proposed as a technique for the anonymization of requests in location based services. This paper provides a formal characterization of a privacy attack that has been informally described in previous work, and presents a new generalization algorithm that is proved to be a safe defense against that attack. The paper(More)
One of the privacy threats recognized in the use of LBS is represented by an adversary having information about the presence of individuals in certain locations, and using this information together with an (anonymous) LBS request to re-identify the issuer of the request associating her to the requested service. Several papers have proposed techniques to(More)
A “friend finder” is a Location Based Service (LBS) that informs users about the presence of participants in a geographical area. In particular, one of the functionalities of this kind of application, reveals the users that are in proximity. Several implementations of the friend finder service already exist but, to the best of our knowledge, none of them(More)
The evaluation of privacy-preserving techniques for LBS is often based on simulations of mostly random user movements that only partially capture real deployment scenarios. We claim that benchmarks tailored to specific scenarios are needed, and we report preliminary results on how they may be generated through an agent-based contextaware simulator. We(More)