| Title | Privacy-Preserving Proximity Detection Framework for Location-Based Services |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yang, Chen, Jia, Zhen, Li, Shundong |
| Conference Name | 2021 International Conference on Networking and Network Applications (NaNA) |
| Keywords | composability, compositionality, data privacy, homomorphic encryption, Human Behavior, location based services(LBSs), location privacy, Location Privacy in Wireless Networks, Metrics, Mobile communication, Performance analysis, privacy, privacy preserving, private proximity detecting, pubcrawl, resilience, Resiliency, Sensors, Servers, Wireless communication, wireless networks |
| Abstract | With the popularization of mobile communication and sensing equipment, as well as the rapid development of location-aware technology and wireless communication technology, LBSs(Location-based services) bring convenience to people's lives and enable people to arrange activities more efficiently and reasonably. It can provide more flexible LBS proximity detection query, which has attracted widespread attention in recent years. However, the development of proximity detection query still faces many severe challenges including query information privacy. For example, when users want to ensure their location privacy and data security, they can get more secure location-based services. In this article, we propose an efficient and privacy-protecting proximity detection framework based on location services: PD(Proximity Detection). Through PD, users can query the range of arbitrary polygons and obtain accurate LBS results. Specifically, based on homomorphic encryption technology, an efficient PRQ(polygon range query) algorithm is constructed. With the help of PRQ, PD, you can obtain accurate polygon range query results through the encryption request and the services provided by the LAS(LBS Agent Server) and the CS(Cloud Server). In addition, the query privacy of the queryer and the information of the data provider are protected. The correctness proof and performance analysis show that the scheme is safe and feasible. Therefore, our scheme is suitable for many practical applications. |
| DOI | 10.1109/NaNA53684.2021.00025 |
| Citation Key | yang_privacy-preserving_2021 |
Privacy-Preserving Proximity Detection Framework for Location-Based Services