A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition
Summary: DPFL-based data marketplace with price-taking and price-setting owners, framed as a three-stage Stackelberg game to maximize requester profit under differential privacy. Convex with a unique SPNE; iterative algorithms; experiments show price competition lowers prices and increases profitability versus price-taking-only baselines. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Peng Sun
- 2. Liantao Wu
- 3. Zhibo Wang
- 4. Jinfei Liu
- 5. Juan Luo
- 6. Wenqiang Jin
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,107 | Reliable and Private Utility Signaling for Data Markets | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,891 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010194092 |
| 3,836 | Dealer: An End-to-End Model Marketplace with Differential Privacy | 2021 | VLDB | 6.7153977e-05 |
| 4,753 | Secure Shapley Value for Cross-Silo Federated Learning | 2023 | VLDB | 5.9469115e-05 |
| 4,805 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB | 5.9102798e-05 |
| 4,863 | Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia | 2023 | SIGMOD | 5.8697471e-05 |
| 5,775 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB | 5.3310992e-05 |
| 6,459 | Practical Differentially Private and Byzantine-resilient Federated Learning | 2023 | SIGMOD | 5.0556005e-05 |
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