Efficient and Privacy-Preserving Geo-Social-Based POI Recommendation Over Encrypted Data
Qi Xu, Hui Zhu, Yandong Zheng, Fengwei Wang, Rongxing Lu, Le Gao · 2023
With the development of location based service and online social networking, geo-social-based points of interest (POIs) recommendation has received wide attention, which comprehensively considers the geographic and social factors. The popularity of cloud computing techniques have driven the emerging trend of outsourcing the geo-social-based POI recommendation service to the cloud. However, the cloud server is not fully trusted, leading to the raising concerns of data privacy. Although many privacy-preserving schemes have been proposed for the geo-social-based POI recommendation, they can only return approximate query results. Aiming at addressing this issue, in this paper, we propose an efficient and privacy-preserving geo-social-based POI recommendation scheme, called TRIPE, with accurate query results. Specifically, we first leverage the Quadtree to organize the geographic data and the MinHash method to index the social data. Then, we design a Quadtree-based POI filtering algorithm and a MinHash-based POI filtering algorithm to filter out some POIs that do not meet geo-social POI recommendation threshold. Meanwhile, we employ the BGV homomorphic encryption to protect the privacy of Quadtree-based/MinHash-based POI filtering algorithms and propose our TRIPE scheme based on these algorithms. Security analysis shows that TRIPE is privacy-preserving, and experimental results show that TRIPE is efficient.