PLPR: Towards Efficient and Privacy-Preserving LBSNs-Based POI Recommendation in Cloud
Lili Sun, Yonggang Zhang, Yandong Zheng, Rongxing Lu, Hui Zhu · 2023
With the popularity of location-based social networks (LBSNs), locations and social relationships have been considered to be important factors in point-of-interest (POI) recommendation services. The boom of cloud computing has driven data owners to outsource the LBSN data and the POI recommendation services to the cloud with powerful computing and storage capabilities. However, as the data usually contains sensitive information, it should be encrypted before being out-sourced, and consequently, the POI recommendation has to be processed over encrypted data. Although several privacy-preserving LBSNs-based POI recommendation schemes have been proposed, they are either inapplicable to the outsourcing scenario or have issues with the recommendation accuracy. Aiming at addressing these issues, in this paper, we propose an efficient and privacy-preserving LBSNs-based POI recommendation scheme (PLPR). Specifically, we first index the users' social relationships with Bloom filters and then organize the user resident location and social relationship dataset into a Vantage Point (VP) tree. Then, we design an efficient LSBNs-based POI recommendation algorithm based on the VP tree. After that, we design a privacy-preserving range determination protocol (PRD) and a privacy-preserving neighbor determination protocol (PND) to respectively protect the privacy of locations and social relationships in the designed algorithm and propose our PLPR scheme. Security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is also efficient.