Proximity detection is one of the critical components in Location-based Social Networks (LBSNS), which has attracted much attention recently. With the advent of LBSNS, more and more users' location information will be collected by the service providers. However, with a potentially untrusted server, such a proximity detection service may threaten the privacy of users. In this paper, aiming at achieving enhanced privacy against the untrusted service providers in LBSNS, we introduce a new architecture with dual-servers for the first time and propose a privacy-preserving proximity detection method based on Geohash. In our architecture, the location coordinates of users are converted into a bit-string by dichotomy approximation, and divided into two subsets: prefix and suffix. The social network server firstly selects the candidate neighbors only in the light of the prefix, and then a third-party server is introduced to compute the relative distance of candidate neighbors according to the suffix. Each server can only get a subset of location code, instead of the whole location information of users as the previous work. We also prove that the new construction is secure under the untrusted server model with enhanced privacy. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.


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    Title :

    Private and Flexible Proximity Detection Based on Geohash


    Contributors:
    Ye, Ayong (author) / Chen, Qiuling (author) / Xu, Li (author)


    Publication date :

    2017-06-01


    Size :

    601416 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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