A novel destination prediction attack and corresponding location privacy protection method in geo-social networks. (January 2017)

• Record Type: Journal Article
• Authors: Xue, Di; Wu, Li-Fa; Li, Hua-Bo; Hong, Zheng; Zhou, Zhen-Ji
• Is Part Of: International journal of distributed sensor networks. Volume 13:Number 1(2017)
• Publication Date: 2017-01
• Subjects: Geo-social networks -- location privacy -- destination prediction -- data sparsity problem -- data mining; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147716685421
• Languages: English
• ISSNs: 1550-1329

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.