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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Main Authors: Bin Liu, Chunyong Zhang, Liangwei Yao, Yang Xin
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Entropy
Subjects:
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.
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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