A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2017-10-23Permanent link to this record
http://hdl.handle.net/10754/625939
Metadata
Show full item recordAbstract
Point-of-Interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping LBS providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trust-oriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trustoriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.Citation
Liu A, Wang W, Li Z, Liu G, Li Q, et al. (2017) A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation. IEEE Access: 1–1. Available: http://dx.doi.org/10.1109/ACCESS.2017.2765317.Sponsors
The work described in this paper was partially supported by KAUST and the National Natural Science Foundation of China (No. 61572336, No. 61402313, No. 61702227, No. 61572335, and No.61472337).Journal
IEEE AccessAdditional Links
http://ieeexplore.ieee.org/document/8078176/ae974a485f413a2113503eed53cd6c53
10.1109/ACCESS.2017.2765317