Journal article

A privacy-enhancing model for location-based personalized recommendations

J Huang, J Qi, Y Xu, J Chen

Distributed and Parallel Databases | Springer | Published : 2015

Abstract

To receive personalized recommendation, users of a location-based service (e.g., a Location-Based Social Network, LBSN) have to provide personal information and preferences to the location-based service. However, detailed personal information could be used to identify the users, and hence compromise user privacy. In this paper, we consider an untrusted third party recommendation service used by the location-based service that may attempt to identify the sender of a recommendation query from the query log or may publish the query log. To protect user identity, anonymization must be done "online" before a query reaches the recommendation service. This is different from the usual "offline" scen..

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Grants

Awarded by National Natural Science Foundation of China


Awarded by National Key Technology R&D Program of China


Awarded by Science-Technology Project of DEGP


Awarded by Natural Science Foundation of Guangdong Province, China


Awarded by Fundamental Research Funds for the Central Universities, SCUT


Funding Acknowledgements

Dr. Jin Huang is supported by the National Natural Science Foundation of China (Grant No. 61370229), the National Key Technology R&D Program of China (Grant No. 2013BAH72B01), and the Science-Technology Project of DEGP (Grant No.2012KJCX0037). A/Prof. Yabo Xu is supported by the National Natural Science Foundation of China (Grant No. 61100003). Prof. Jian Chen is supported by the National Natural Science Foundation of China (Grant No. 61272065), the Natural Science Foundation of Guangdong Province, China (Grant No. S2012010009311), and the Fundamental Research Funds for the Central Universities, SCUT (Grant No. 2012ZZ0088).