Service-Splitting-Based Privacy Protection Mechanism for Proximity Detection Supporting High Utility

Chen, Qiuling; Ye, Ayong; Cheng, Baorong; Huang, Chuan Zhen

doi:10.1109/tcss.2022.3147597

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…To address the problem of over-reliance on the security and trustworthiness of ASs, Chen et al [49] proposed a two-server architecture with service splitting. They divided the Geohash code into two parts: a prefix and suffix.…”

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

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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.

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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

show abstract

“…1443Aug. -1454 Huang, C., see Chen, Q., TCSS Feb. 2023 192-203 Huang, G., see Chen, J., TCSS April 2023 492-506 Huang, G., see Chen, J., 2523-2535 Huang, H., see Yao, X., 2698-2712 Huang, L., see Tian, G., TCSS April 2023 700-713 Huang, M., see He, Q., 2244-2253 Huang, S., see Chen, Z., 2739-2751 Huet, S., see Deffuant, G., TCSS June 2023 922-933 Huo, D., see Zhao, P., TCSS Aug. 1875Aug.…”

Section: Formal Concept Analysismentioning

confidence: 99%

“…1924Aug. -1926 Tang, Z., Chen, Q., Sun, Z., Xiong, P., Zhang, B., Jiang, L., and Wu, E.Q 1927-1938 Messages; TCSS Feb. 2023 10-23 Tian, F., see An, W., TCSS Feb. 2023 403-412 Tian, G., Qian, K., Li, X., Sun, M., Jiang, H., Qiu, W., Xie, X., Zhao, Z., Huang, L., Luo, S., Guo, T., Cai, R., Wang, Z., 1966-1974 Wang, H., see 1813-1825 Wang, H., Wu, Y., Xu, T., Shen, Y., Yang, Y., Xie, H., He, J., and Cai, H Li, M., Wang, E., Tang, J., and Hu, B., The Impact of Computational Drug Discovery on Society; TCSS Oct. 2023 2148-2159 Wang, J., Sun, X., Chen, Q., and Wang, M., Information-Enhanced Hierarchical Self-Attention Network for Multiturn Dialog Generation; TCSS Oct. 2023 2686-2697 Wang, J., Yang, Y., Liu, Q., Fang, Z., Sun, S., and Xu, Y., An 1975-1985 Ye, A., see Chen, Q., TCSS Feb. 2023 192-203 Ye, S., see Liu, X., TCSS Feb. 2023…”

mentioning

confidence: 99%

2023 Index IEEE Transactions on Computational Social Systems Vol. 10

2023

IEEE Trans. Comput. Soc. Syst.

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This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2023, and items from previous years that were commented upon or corrected in 2023. Departments and other items may also be covered if they have been judged to have archival value.The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

show abstract

Service-Splitting-Based Privacy Protection Mechanism for Proximity Detection Supporting High Utility

Cited by 2 publications

References 26 publications

GLPS: A Geohash-Based Location Privacy Protection Scheme

GLPS: A Geohash-Based Location Privacy Protection Scheme

2023 Index IEEE Transactions on Computational Social Systems Vol. 10

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