GLPS: A Geohash-Based Location Privacy Protection Scheme
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 conte...
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MDPI AG
2023-11-01
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Series: | Entropy |
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Online Access: | https://www.mdpi.com/1099-4300/25/12/1569 |
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author | Bin Liu Chunyong Zhang Liangwei Yao Yang Xin |
author_facet | Bin Liu Chunyong Zhang Liangwei Yao Yang Xin |
author_sort | Bin Liu |
collection | DOAJ |
description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula>. On the user side, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> 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 <i>k</i>-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> and compared it with three schemes: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>a</mi><mi>s</mi><mi>p</mi><mi>e</mi><mi>r</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>C</mi><mi>a</mi><mi>s</mi><mi>p</mi><mi>e</mi><mi>r</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>L</mi><mi>S</mi></mrow></semantics></math></inline-formula>. The experimental results and analyses demonstrate that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> 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. |
first_indexed | 2024-03-08T20:47:26Z |
format | Article |
id | doaj.art-0a0a1e6ff017454f80a34a8d0a55cfd7 |
institution | Directory Open Access Journal |
issn | 1099-4300 |
language | English |
last_indexed | 2024-03-08T20:47:26Z |
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publisher | MDPI AG |
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spelling | doaj.art-0a0a1e6ff017454f80a34a8d0a55cfd72023-12-22T14:07:12ZengMDPI AGEntropy1099-43002023-11-012512156910.3390/e25121569GLPS: A Geohash-Based Location Privacy Protection SchemeBin Liu0Chunyong Zhang1Liangwei Yao2Yang Xin3National Engineering Research Center for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Research Center for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Research Center for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Research Center for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWith 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula>. On the user side, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> 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 <i>k</i>-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> and compared it with three schemes: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><mi>a</mi><mi>s</mi><mi>p</mi><mi>e</mi><mi>r</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>C</mi><mi>a</mi><mi>s</mi><mi>p</mi><mi>e</mi><mi>r</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>L</mi><mi>S</mi></mrow></semantics></math></inline-formula>. The experimental results and analyses demonstrate that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>G</mi><mi>L</mi><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/1099-4300/25/12/1569location-based serviceslocation privacy protectionGeohash codelocation semantics |
spellingShingle | Bin Liu Chunyong Zhang Liangwei Yao Yang Xin GLPS: A Geohash-Based Location Privacy Protection Scheme Entropy location-based services location privacy protection Geohash code location semantics |
title | GLPS: A Geohash-Based Location Privacy Protection Scheme |
title_full | GLPS: A Geohash-Based Location Privacy Protection Scheme |
title_fullStr | GLPS: A Geohash-Based Location Privacy Protection Scheme |
title_full_unstemmed | GLPS: A Geohash-Based Location Privacy Protection Scheme |
title_short | GLPS: A Geohash-Based Location Privacy Protection Scheme |
title_sort | glps a geohash based location privacy protection scheme |
topic | location-based services location privacy protection Geohash code location semantics |
url | https://www.mdpi.com/1099-4300/25/12/1569 |
work_keys_str_mv | AT binliu glpsageohashbasedlocationprivacyprotectionscheme AT chunyongzhang glpsageohashbasedlocationprivacyprotectionscheme AT liangweiyao glpsageohashbasedlocationprivacyprotectionscheme AT yangxin glpsageohashbasedlocationprivacyprotectionscheme |