Privacy-preserving Cooperative Online Matching over Spatial Crowdsourcing Platforms
Summary: Introduces PCOM, a framework for privacy-preserving cooperative online matching across spatial crowdsourcing platforms that protects worker/requester locations and platform histories while enabling cross-platform task assignment. Provides differential-privacy guarantees, two algorithmic strategies with proofs, and empirical validation on real and synthetic datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yi Yang
- 2. Yurong Cheng
- 3. Ye Yuan
- 4. Guoren Wang
- 5. Lei Chen
- 6. Yongjiao Sun
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,179 | A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing | 2014 | VLDB | 9.3558219e-05 |
| 3,235 | Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis | 2016 | VLDB | 7.3355287e-05 |
| 9,443 | Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach | 2018 | SIGMOD | 4.3408772e-05 |
| 11,794 | Flexible Online Task Assignment in Real-Time Spatial Data | 2017 | VLDB | 4.1945683e-05 |
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