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 theoretical and experimental analysis of the existing solutions for the location privacy problem in proximity services. This is a serious problem for users who do not trust the SP to handle their location data, and would only like to release their location information in a generalized form to participating buddies. The paper presents two new protocols providing complete privacy with respect to the SP, and controllable privacy with respect to the buddies. The analytical and experimental analysis of the protocols takes into account privacy, service precision, and computation and communication costs, showing the superiority of the new protocols compared to those appeared in the literature to date. The proposed protocols have also been tested in a full system implementation of the proximity service.
Geo-Social Networks (GeoSNs) extend social networks by providing context-aware services that support the association of location with users and content. We are witnessing a proliferation of GeoSNs, and indications are that these are rapidly attracting increasing numbers of users. The availability of user location yields new capabilities that provide benefits to users as well as service providers. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins." However, the introduction of location generates new privacy threats, which in turn calls for new means of affording user privacy in GeoSNs.This article categorizes GeoSNs according to the services they offer; it studies three privacy aspects that are central to GeoSNs, namely location, absence, and co-location privacy; and it discusses possible means of providing these kinds of privacy, as well as presents unresolved privacy-related challenges in GeoSNs.
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 privacy-aware proximity based services. This paper describes the main privacy threats that the usage of these services can lead to, and proposes original privacy preservation techniques offering different trade-offs between quality of service and privacy preservation. The properties of the proposed algorithms are formally proved, and an extensive experimental work illustrates the practicality of the approach.
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 threats beyond those found in location-based services. Content published in a GeoSN is often associated with references to multiple users, without the publisher being aware of the privacy preferences of those users. Moreover, this content is often accessible to multiple users. This renders it difficult for GeoSN users to control which information about them is available and to whom it is available. This paper addresses two privacy threats that occur in GeoSNs: location privacy and absence privacy. The former concerns the availability of information about the presence of users in specific locations at given times, while the latter concerns the availability of information about the absence of an individual from specific locations during given periods of time. The challenge addressed is that of supporting privacy while still enabling useful services. We believe this is the first paper to formalize these two notions of privacy and to propose techniques for enforcing them. The techniques offer privacy guarantees, and the paper reports on empirical performance studies of the techniques.
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