Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-indistinguishable Approach

IEEE Transactions on Information Forensics & Security (TIFS), 2025

Pengfei Zhang1 Xiang Cheng2 Zhikun Zhang3 Youwen Zhu4 Ji Zhang5

1. State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining and the School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China 2. Beijing University of Posts and Telecommunications, Beijing, China 3. School of Computer Science and Technology, Zhejiang University, Hangzhou, China 4. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China 5. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia

Abstract


Geo-indistinguishability provides strong local differential privacy guarantees for location-based worker recruitment, but naively perturbing locations often hurts area coverage. We study privacy-preserving worker recruitment with the objective of maximizing covered regions under geo-indistinguishable perturbation. We propose a prior knowledge-enhanced approach that models worker uncertainty and optimizes recruitment decisions with privacy constraints. Experiments on real-world datasets show better coverage-utility trade-offs than existing privacy-preserving recruitment baselines.

Resources


Citation 

 @inproceedings{ZCZZZ25,
    author = {Pengfei Zhang and Xiang Cheng and Zhikun Zhang and Youwen Zhu and Ji Zhang},
    title = {{Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-indistinguishable Approach}},
    booktitle = {{Transactions on Information Forensics and Security}},
    publisher = {IEEE},
    year = {2025},
}