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.

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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Cooperative Collision Warning Systems (CCWSs) have become a major vehicle safety application in intelligent transportation systems. Vehicles organized in a vehicular ad-hoc network use a CCWS communication protocol to propagate emergency messages about hazardous events. Police cars, ambulances responding to incidents and speeding cars or motorcycles that constantly vary their speed, change lanes or commit other apparent traffic violations are examples of vehicles that demonstrate hazardous traffic patterns. Using their GPS and motion sensors, vehicles can detect those traveling in nearby avenue sections who constitute a threat.In this paper, we propose a broadcasting protocol that alerts drivers about the presence of moving vehicles demonstrating hazardous driving behavior. In order to limit the volume of redundant transmissions, our approach selects the vehicles to be responsible for transmitting the emergency information for a hazardous vehicle. In this context, we provide mechanisms to create and maintain a chain of transmitters. This chain "covers" the road sections on which a hazardous vehicle is moving.Our protocol attempts to increase the probability that an endangered vehicle does obtain timely information about a hazardous vehicle and reduce the total communication traffic imposed in urban environments where the vehicles' density is often high. We experimentally evaluate our suggested protocol by comparing it with two alternative CCWS broadcasting approaches and we ascertain the extent in which the above objectives are met.

Private matching (or join) of spatial datasets is crucial for applications where distinct parties wish to share information about nearby geo-tagged data items. To protect each party's data, only joining pairs of points should be revealed, and no additional information about non-matching items should be disclosed. Previous research efforts focused on private matching for relational data, and rely either on spaceembedding or on SMC techniques. Space-embedding transforms data points to hide their exact attribute values before matching is performed, whereas SMC protocols simulate complex digital circuits that evaluate the matching condition without revealing anything else other than the matching outcome.However, existing solutions have at least one of the following drawbacks: (i) they fail to protect against adversaries with background knowledge on data distribution, (ii) they compromise privacy by returning large amounts of false positives and (iii) they rely on complex and expensive SMC protocols. In this paper, we introduce a novel geometric transformation to perform private matching on spatial datasets. Our method is efficient and it is not vulnerable to background knowledge attacks. We consider two distance evaluation metrics in the transformed space, namely L 2 and L ∞ , and show how the metric used can control the trade-off between privacy and the amount of returned false positives. We provide an extensive experimental evaluation to validate the precision and efficiency of our approach.