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Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access information that is relevant to their present location. Users are interested in querying about points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping such information up-to-date and relevant to the users is not an easy task, so the owners of such datasets will make the data accessible only to paying customers. Users send their current location as the query parameter, and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). But typical data owners do not have the technical means to support processing queries on a large scale, so they outsource data storage and querying to a cloud service provider. Many such cloud providers exist who offer powerful storage and computational infrastructures at low cost. However, cloud providers are not fully trusted, and typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to answer queries correctly, but they also collect the locations of the POIs and the subscribers for other purposes. Leakage of POI locations can lead to privacy breaches as well as financial losses to the data owners, for whom the POI dataset is an important source of revenue. Disclosure of user locations leads to privacy violations and may deter subscribers from using the service altogether. In this paper, we propose a family of techniques that allow processing of NN queries in an untrusted outsourced environment, while at the same time protecting both the POI and querying users’ positions. Our techniques rely on mutable order preserving encoding (mOPE), the only secure order-preserving encryption method known to-date. We al

Mobile users engage in novel and exciting location-based social media applications (e.g., geosocial networks, spatial crowdsourcing) in which they interact with other users situated in their proximity. In several application scenarios, users define their own proximity zones of interest (typically in the form of polygonal regions, such as a collection of city blocks), and want to find other users with whom they are in a mutual enclosure relationship with respect to their respective proximity zones. This boils down to evaluating two point-in-polygon enclosure conditions, which is easy to achieve for revealed user locations and proximity zones. However, users may be reluctant to share their whereabouts with their friends and with social media service providers, as location data can help one infer sensitive details such as an individual's health status, financial situation or lifestyle choices. In this paper, we propose a mechanism that allows users to securely evaluate mutual proximity zone enclosure on encrypted location data. Our solution uses homomorphic encryption, and supports convex polygonal proximity zones. We provide a security analysis of the proposed solution, we investigate performance optimizations, and we show experimentally that our approach scales well for datasets of millions of users.

37th Annual IEEE Conference on Local Computer NetworksClearwater Beach, FL, USA USA October 22-October 25