A novel destination prediction attack and corresponding location privacy protection method in geo-social networks

Di Xue, Li-Fa Wu, Hua-Bo Li, Zheng Hong and Zhen-Ji Zhou

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 1, 1550147716685421

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.

Keywords: Geo-social networks; location privacy; destination prediction; data sparsity problem; data mining (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:1:p:1550147716685421

DOI: 10.1177/1550147716685421

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