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Protecting multi-party privacy in location-aware social point-of-interest recommendation

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Abstract

Point-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (e.g., book and movie) recommendation, which leads to more serious cold start problem. Social POI recommendation has proved to be an effective solution, but most existing works assume that recommenders have access to all required data. This is very rare in practice because these data are generally owned by different entities who are not willing to share their data with others due to privacy and legal concerns. In this paper, we first propose PLAS, a protocol which enables effective POI recommendation without disclosing the sensitive data of every party getting involved in the recommendation. We formally show PLAS is secure in the semi-honest adversary model. To improve its performance. We then adopt the technique of cloaking area by which expensive distance computation over encrypted data is replaced by cheap operation over plaintext. In addition, we utilize the sparsity of check-ins to selectively publish data, thus reducing encryption cost and avoiding unnecessary computation over ciphertext. Experiments on two real datasets show that our protocol is feasible and can scale to large POI recommendation problems in practice.

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Notes

  1. https://snap.stanford.edu/data/loc-gowalla.html

  2. https://www.yelp.com/datasetchallenge

  3. https://www.keylength.com/

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Acknowledgements

Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61572335, 61402313)

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Weiqi Wang, An Liu & Zhixu Li

  2. King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

    Xiangliang Zhang

  3. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Qing Li

  4. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Xiaofang Zhou

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  1. Weiqi Wang
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  2. An Liu
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  3. Zhixu Li
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  4. Xiangliang Zhang
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Correspondence to An Liu.

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This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang

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Wang, W., Liu, A., Li, Z. et al. Protecting multi-party privacy in location-aware social point-of-interest recommendation. World Wide Web 22, 863–883 (2019). https://doi.org/10.1007/s11280-018-0550-9

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  • DOI: https://doi.org/10.1007/s11280-018-0550-9

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