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

  • Weiqi Wang
  • An Liu
  • Zhixu Li
  • Xiangliang Zhang
  • Qing Li
  • Xiaofang Zhou
Article
  • 35 Downloads
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

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.

Keywords

Location privacy Point-of-interest Recommendation 

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Weiqi Wang
    • 1
  • An Liu
    • 1
  • Zhixu Li
    • 1
  • Xiangliang Zhang
    • 2
  • Qing Li
    • 3
  • Xiaofang Zhou
    • 4
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  3. 3.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong
  4. 4.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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