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)
Incoming Non-self Citations Over Time
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Authors
- 1. Peng Sun (Hunan University)
- 2. Liantao Wu (East China Normal University)
- 3. Zhibo Wang (Zhejiang University)
- 4. Jinfei Liu (Zhejiang University)
- 5. Juan Luo (Hunan University)
- 6. Wenqiang Jin (Hunan University)
BibTeX Citation
@inproceedings{sun_sigmod24,
title = {{A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition}},
author = {Sun, Peng and Wu, Liantao and Wang, Zhibo and Liu, Jinfei and Luo, Juan and Jin, Wenqiang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3677127},
url = {https://dl.acm.org/doi/10.1145/3677127},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,666 | Reliable and Private Utility Signaling for Data Markets | 2026 | SIGMOD | 4.9605343e-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 |
|---|---|---|---|---|
| 1,369 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010876597 |
| 3,694 | Secure Shapley Value for Cross-Silo Federated Learning | 2023 | VLDB | 7.0721664e-05 |
| 3,789 | Dealer: An End-to-End Model Marketplace with Differential Privacy | 2021 | VLDB | 6.998403e-05 |
| 4,558 | Projected Federated Averaging with Heterogeneous Differential Privacy | 2022 | VLDB | 6.5249713e-05 |
| 4,930 | Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia | 2023 | SIGMOD | 6.3383989e-05 |
| 5,736 | Federated Matrix Factorization with Privacy Guarantee | 2022 | VLDB | 5.995594e-05 |
| 6,508 | Practical Differentially Private and Byzantine-resilient Federated Learning | 2023 | SIGMOD | 5.7451686e-05 |
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