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A privacy-enhancing model for location-based personalized recommendations

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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” scenario where a trusted recommendation service will receive all unanonymized queries and the focus is to anonymize the collected query log. We propose the notion of online anonymity to formalize this online requirement. The challenge for providing online anonymity is dealing with unknown and dynamic location-based service users who can get online and offline at any time. We define this problem, discuss its implications and differences from the problems in the literature, and propose a solution. Our experimental study shows that it is feasible to achieve personalized recommendation while preserve user privacy.

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Notes

  1. http://www.choicestream.com/news/.

  2. http://www.anonymizer.com.

  3. http://www.openid.net.

  4. http://archive.ics.uci.edu/ml/.

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Acknowledgments

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).

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Authors and Affiliations

  1. School of Computer, South China Normal University, Guangzhou, China

    Jin Huang

  2. Shenzhen Engineering Laboratory for Mobile Internet Application Middleware Technology, Shenzhen, China

    Jin Huang

  3. Department of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia

    Jianzhong Qi

  4. School of Software, Sun Yat-Sen University, Guangzhou, China

    Yabo Xu

  5. School of Software Engineering, South China University of Technology, Guangzhou, China

    Jian Chen

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  1. Jin Huang
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Correspondence to Yabo Xu.

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Huang, J., Qi, J., Xu, Y. et al. A privacy-enhancing model for location-based personalized recommendations. Distrib Parallel Databases 33, 253–276 (2015). https://doi.org/10.1007/s10619-014-7148-8

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