GLPS: A Geohash-Based Location Privacy Protection Scheme - Bin Liu; Chunyong Zhang; Liangwei Yao; Yang Xin (Entropy, 2023)
With the development of mobile applications, location-based services (LBSs) have been incorporated into people’s daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named <semantics>GLPS</semantics>. On the user side, <semantics>GLPS</semantics> uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. <semantics>GLPS</semantics> uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user’s exact location. On the anonymous server side, the scheme takes advantage of Geohash codes’ geospatial gridding capabilities and GL-Tree’s effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user’s location. We experimentally tested the performance of <semantics>GLPS</semantics> and compared it with three schemes: <semantics>Casper</semantics>, <semantics>GCasper</semantics>, and <semantics>DLS</semantics>. The experimental results and analyses demonstrate that <semantics>GLPS</semantics> has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.