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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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,732 | A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing | 2014 | VLDB | 8.2530774e-05 |
| 4,602 | Online Minimum Matching in Real-Time Spatial Data: Experiments and Analysis | 2016 | VLDB | 6.6811778e-05 |
| 9,449 | Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach | 2018 | SIGMOD | 5.3308016e-05 |
| 11,802 | Flexible Online Task Assignment in Real-Time Spatial Data | 2017 | VLDB | 5.1725247e-05 |
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