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

作者：Xue, Di; Wu, Li-Fa^*; Li, Hua-Bo; Hong, Zheng; Zhou, Zhen-Ji

来源：International Journal of Distributed Sensor Networks, 2017, 13(1): 6685421.

DOI：10.1177/1550147716685421

• Geo-social networks
• location privacy
• destination prediction
• data sparsity problem
• data mining

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.

• 出版日期2017-1
• 单位中国人民解放军陆军工程大学