A scoping review of privacy protection in LBS: Architectures, Threats, and Defense mechanisms
Location-based services (LBS) generate large volumes of spatiotemporal data, raising significant privacy concerns. Yet the field lacks a systematic survey that examines security risks across the full LBS data lifecycle and provides a comprehensive comparison of privacy-preserving mechanisms. This scoping review advances LBS privacy by offering a lifecycle-aware synthesis, a rigorous comparative framework, and a forward-looking agenda for research and deployment. Specifically, we: (1) map attack surfaces and quantify privacy–utility trade-offs across collection, storage, transmission, processing, and sharing, with particular attention to server-side threats and untrusted providers; (2) develop a multidimensional framework that compares leading techniques-k-anonymity, differential privacy, perturbation/obfuscation, and encrypted search-in terms of protection strength, computational/communication overhead, and deployment suitability; and (3) identify technical bottlenecks and outline research directions aligned with emerging scenarios and industry trends. Together, these contributions provide actionable criteria for selecting, combining, and deploying robust, efficient LBS privacy solutions.
Authors
- Xingchi Chen (ORCID: https://orcid.org/0009-0007-6296-5122)
- Daxin Zhu (ORCID: https://orcid.org/0000-0002-8060-7368)
- Athanasios V. Vasilakos (ORCID: https://orcid.org/0000-0003-1902-9877)
- Fa Zhu (ORCID: https://orcid.org/0000-0002-7089-5386)
- Huilai Zhi (ORCID: https://orcid.org/0000-0002-3598-9053)
- Yuxi Li (ORCID: https://orcid.org/0000-0001-7921-8720)
- Dong Ji (ORCID: https://orcid.org/0000-0003-1341-5638)
- Jingjing Chen (ORCID: https://orcid.org/0009-0009-9815-9362)
Institutions
- Foshan University (CN)
- Nanjing Forestry University (CN)
- Quanzhou Normal University (CN)
- University of Agder (NO)
- Peng Cheng Laboratory (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Journal of King Saud University - Computer and Information Sciences
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1007/s44443-025-00337-3
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Natural Science Foundation of Liaoning Province
- Fundamental Research Funds for the Central Universities
- Basic and Applied Basic Research Foundation of Guangdong Province