Abstract
Location publication in check-in services of geo-social networks raises serious privacy concerns due to rich sources of background information. This article proposes a novel destination prediction approach Destination Prediction specially for the check-in service of geo-social networks, which not only addresses the “data sparsity problem” faced by common destination prediction approaches, but also takes advantages of the commonly available background information from geo-social networks and other public resources, such as social structure, road network, and speed limits. Further considering the Destination Prediction–based attack model, we present a location privacy protection method Check-in Deletion and framework Destination Prediction + Check-in Deletion to help check-in users detect potential location privacy leakage and retain confidential locational information against destination inference attacks without sacrificing the real-time check-in precision and user experience. A new data preprocessing method is designed to construct a reasonable complete check-in subset from the worldwide check-in data set of a real-world geo-social network without loss of generality and validity of the evaluation. Experimental results show the great prediction ability of Destination Prediction approach, the effective protection capability of Check-in Deletion method against destination inference attacks, and high running efficiency of the Destination Prediction + Check-in Deletion framework.
Highlights
Driven by the explosive increase of online social media and modern mobile devices with integrated position sensors, such as smart phones and tablet PC, geo-social networks (GeoSNs) have attracted millions of users in recent years
We focus on the basic location privacy problem in GeoSNs and limit our attention to destination prediction attack, a sort of location privacy attack from which the adversary could infer the most likely place to be visited at certain time in the near future by the target user
Based on the above inference model and protection method, we propose a location privacy protection framework DPCD to guard against destination inference attacks
Summary
Driven by the explosive increase of online social media and modern mobile devices with integrated position sensors, such as smart phones and tablet PC, geo-social networks (GeoSNs) have attracted millions of users in recent years. A novel approach to personalized destination prediction, named DesPre, is proposed specially for check-in services in GeoSNs, which addresses the ‘‘data sparsity problem’’ faced by other destination prediction approaches, and takes advantage of the commonly available background information from the GeoSNs so that the most probable locations to be visited by the target user can be inferred accurately even when the available historical trajectories are too sparse to cover all the possible trajectories. Making use of the DesPre approach as the privacy attack model, we present a new privacy protection method CkiDel, which prevents adversaries from obtaining the correct patterns of users’ movements by deleting the smallest number of users’ historical check-ins so that the real sensitive destinations of target users would not be available without sacrificing the real-time check-in precision and user experience. Experimental evaluations are discussed in section ‘‘Experimental evaluation.’’ Section ‘‘Conclusion and future work’’ concludes the article and presents our future work
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