Location Privacy Protection Method Based on Geohash Coding and Pseudo-Random Sequence

Liu, Kai; Han, Yiliang; Wang, Jingjing; Kaiyang, Guo

doi:10.1109/ictc55111.2022.9778536

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wei [ 26 ] achieves location retrieval in the neighborhood POI problem by Geohash encoding the user’s spatial two-dimensional coordinates while hiding the user’s exact location. Liu et al [ 27 ] used the Geohash code and pseudo-random sequence selection algorithm to select the geographical intervals involved in anonymous hiding from the location set and designed a k-anonymous location privacy protection scheme. Ye et al [ 28 ] proposed a proximity detection method based on Geohash for privacy preservation.…”

Section: Related Workmentioning

confidence: 99%

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

Liu

Zhang

Xin

2023

Sensors

View full text Add to dashboard Cite

Location-based application services and location privacy protection solutions are often required for the storage, management, and efficient retrieval of large amounts of geolocation data for specific locations or location intervals. We design a hierarchical tree-like organization structure, GL-Tree, which enables the storage, management, and retrieval of massive location data and satisfies the user’s location-hiding requirements. We first use Geohash encoding to convert the two-dimensional geospatial coordinates of locations into one-dimensional strings and construct the GL-Tree based on the Geohash encoding principle. We gradually reduce the location intervals by extending the length of the Geohash code to achieve geospatial grid division and spatial approximation of user locations. The hierarchical tree structure of GL-Tree reflects the correspondence between Geohash codes and geographic intervals. Users and their location relationships are recorded in the leaf nodes at each level of the hierarchical GL-Tree. In top–down order, along the GL-Tree, efficient storage and retrieval of location sets for specified locations and specified intervals can be achieved. We conducted experimental tests on the Gowalla public dataset and compared the performance of the B+ tree, R tree, and GL-Tree in terms of time consumption in three aspects: tree construction, location insertion, and location retrieval, and the results show that GL-Tree has good performance in terms of time consumption.

show abstract

Section: Related Workmentioning

confidence: 99%

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

Liu

Zhang

Xin

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Gao et al [45] encoded trajectories covering different grids and designed Geohash-Tree query trees to speed up the efficiency of querying. Liu et al [46] proposed a location privacy protection scheme combining Geohash and pseudo-random sequence techniques. Xing et al [47] designed the G-Casper scheme, which uses the user's Geohashencoded location grid and its surrounding eight grids as candidate grids, and selected one or more grids to construct the hidden intervals through the processing the random grid numbering, checking the number of users, and pruning.…”

Section: Research On Geohash In Location Services and Privacy Protectionmentioning

confidence: 99%

GLPS: A Geohash-Based Location Privacy Protection Scheme

Liu,

Zhang,

Yao

et al. 2023

Entropy

View full text Add to dashboard Cite

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 GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. GLPS 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 GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS 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.

show abstract

Location Privacy Protection Method Based on Geohash Coding and Pseudo-Random Sequence

Cited by 2 publications

References 4 publications

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

GLPS: A Geohash-Based Location Privacy Protection Scheme

Contact Info

Product

Resources

About