An Exact and Efficient Privacy-Preserving Spatiotemporal Matching in Mobile Social Networks

Xiuguang Li, Yuanyuan He, Ben Niu, Kai Yang and Hui Li
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Xiuguang Li: National Key Laboratory of Integrated Networks Services, Xidian University, Xi'an, China
Yuanyuan He: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Ben Niu: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Kai Yang: Key Laboratory of Information and Network Security of Chinese Armed Police Force, Engineering University of Chinese Armed Police Force, Xi'an, China
Hui Li: National Key Laboratory of Integrated Networks Services, Xidian University, Xi'an, China

International Journal of Technology and Human Interaction (IJTHI), 2016, vol. 12, issue 2, 36-47

Abstract: With the rapid development of mobile smartphone and its built-in location-aware devices, people are possible to establish trust relationships and further interaction with each other based their matched interests, hobbies, experiences, or spatiotemporal profiles. However, the possibility of sensitive information leakage and heavy computation overhead constrain the widespread use of the matching schemes in mobile social networks. Many privacy-preserving matching schemes were proposed recently years, but how to achieve privacy-preserving spatiotemporal matching exactly and efficiently remains an open question. In this paper, the authors propose a novel spatiotemporal matching scheme. The overlapping grid system is introduced into the scheme to improve the accuracy of spatiotemporal matching, and many repetitive records in a user's spatiotemporal profile are counted as one item so as to cut down the computation overhead. Their scheme decreases the spatiotemporal matching error, and promotes the efficiency of private matchmaking simultaneously. Thorough security analysis and evaluation results indicate that our scheme is effective and efficient.

Date: 2016
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