research-article

Location Privacy-preserving Mechanisms in Location-based Services: A Comprehensive Survey

Authors:

Arun IyengarAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 1

Article No.: 4, Pages 1 - 36

https://doi.org/10.1145/3423165

Published: 02 January 2021 Publication History

Abstract

Location-based services (LBSs) provide enhanced functionality and convenience of ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the users’ privacy. The leakage of private location data in the LBS context has drawn significant attention from academics and industry due to its importance, leading to numerous research efforts aiming to confront the related challenges. However, to the best of our knowledge, none of relevant studies have performed a qualitative and quantitative comparison and analysis of the complex topic of designing countermeasures and discussed the viability of their use with different kinds of services and the potential elements that could be deployed to meet new challenges. Accordingly, the purpose of this survey is to examine the privacy-preserving techniques in LBSs. We categorize and provide an inside-out review of the existing techniques. Performing a retrospective analysis of several typical studies in each category, we summarize their basic principles and recent advances. Additionally, we highlight the use of privacy-preserving techniques in LBSs for enabling new research opportunities. Providing an up-to-date and comprehensive overview of existing studies, this survey may further stimulate new research efforts into this promising field.

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Fränti P(2024)Mopsi location-based serviceApplied Computing and Intelligence10.3934/aci.20240134:2(209-233)Online publication date: 2024
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Index Terms

Location Privacy-preserving Mechanisms in Location-based Services: A Comprehensive Survey
1. Security and privacy
  1. Human and societal aspects of security and privacy

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 1

January 2022

844 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3446641

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 02 January 2021

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 January 2020

Published in CSUR Volume 54, Issue 1

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Molina EFiacchini MGoarant ARaes RCerf SRobu B(2025)Application of a predictive method to protect privacy of mobility dataControl Engineering Practice10.1016/j.conengprac.2024.106223156(106223)Online publication date: Mar-2025
https://doi.org/10.1016/j.conengprac.2024.106223
Fränti P(2024)Mopsi location-based serviceApplied Computing and Intelligence10.3934/aci.20240134:2(209-233)Online publication date: 2024
https://doi.org/10.3934/aci.2024013
Macha MFoutz NLi BGhose A(2024)Personalized Privacy Preservation in Consumer Mobile TrajectoriesInformation Systems Research10.1287/isre.2023.122735:1(249-271)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1287/isre.2023.1227
Tan CLiu ZLi ZJia JLv SLi TLiu Z(2024)EdgeSyn: Privacy-preserving Data Publishing on Edge Network over Infinite Multimedia Data StreamACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3696421Online publication date: 19-Sep-2024
https://doi.org/10.1145/3696421
Ozeki RYonekura HRizk HYamaguchi H(2024)Privacy Preserved Taxi Demand Prediction System for Distributed DataProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691234(123-134)Online publication date: 29-Oct-2024
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Zhao PJiang JZhang G(2024)FedSuper: A Byzantine-Robust Federated Learning Under SupervisionACM Transactions on Sensor Networks10.1145/363009920:2(1-29)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3630099
Min MZhu HLi SZhang HXiao LPan MHan Z(2024)Semantic Adaptive Geo-Indistinguishability for Location Privacy Protection in Mobile NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2024.335488173:6(9193-9198)Online publication date: Jul-2024
https://doi.org/10.1109/TVT.2024.3354881
Shen SHao XGao ZWu GShen YZhang HCao QYu S(2024)SAC-PP: Jointly Optimizing Privacy Protection and Computation Offloading for Mobile Edge ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2024.344775321:6(6190-6203)Online publication date: Dec-2024
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Zhao PMu YYang ZWu JWang BWang Z(2024)Graph-Based Data Publication via Differentially Structural InferenceIEEE Transactions on Network and Service Management10.1109/TNSM.2023.331742021:1(1257-1270)Online publication date: Mar-2024
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Wang CYuan MYang YPeng KJiang H(2024)Revisiting Long- and Short-Term Preference Learning for Next POI Recommendation With Hierarchical LSTMIEEE Transactions on Mobile Computing10.1109/TMC.2024.341740523:12(12693-12705)Online publication date: Dec-2024
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