Abstract
As the core of location-based services (LBS), the LBS-oriented recommendation systems, which suggest the points-of-interest (POIs) to users by analyzing the distribution of the user’s previous points-of-interest, have attracted great interest from both academia and industry. Despite the convenience brought by the LBS-oriented recommendation systems, most of current systems require users to expose their locations, which give rise to a big concerning of the location privacy issues. Meanwhile, as the defacto LBS infrastructure, the mobile-cloud computing paradigm introduces new opportunities and challenges to solve the privacy issues in LBS-oriented recommendation systems. To this end, we propose a novel location-privacy protected scheme for mobile-cloud based recommendation system. The scheme consists of two parts. (1) The server analyzes the user behavior pattern and then makes a list of sketchy recommendation, named as the recommended candidate list. (2) Mobile phone downloads the recommended candidate list from the server and refines the recommendation by taking the current geographical position, current time and location popularity into consideration. With the result from real data driven simulations, the scheme is proved to solve the problem of location privacy risks and improve the accuracy of recommendation.
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Guan, H., Qian, H., Zhao, Y. (2016). Location Privacy Protected Recommendation System in Mobile Cloud. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10039. Springer, Cham. https://doi.org/10.1007/978-3-319-48671-0_36
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DOI: https://doi.org/10.1007/978-3-319-48671-0_36
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