Reliable and Private Utility Signaling for Data Markets
Summary: Defines a utility-signaling primitive ensuring both privacy and input reliability in data markets and proves it prevents suboptimal trading. Realized without a trusted third party via maliciously-secure MPC with MPC hash verification and an optimized MPC KNN‑Shapley for multi-seller valuation; experiments show practicality. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Li Peng (Alibaba; Zhejiang University)
- 2. Jiayao Zhang (Zhejiang University)
- 3. Yihang Wu (Zhejiang University)
- 4. Weiran Liu (Alibaba)
- 5. Jinfei Liu (Hangzhou High-Tech Zone (Binjiang) Blockchain and Data Security Research Institute; Zhejiang University)
- 6. Zheng Yan (Xidian University)
- 7. Kui Ren (Zhejiang University)
- 8. Lei Zhang (Alibaba)
- 9. Lin Qu (Alibaba)
BibTeX Citation
@inproceedings{peng_sigmod26,
title = {{Reliable and Private Utility Signaling for Data Markets}},
author = {Peng, Li and Zhang, Jiayao and Wu, Yihang and Liu, Weiran and Liu, Jinfei and Yan, Zheng and Ren, Kui and Zhang, Lei and Qu, Lin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769821},
url = {https://dl.acm.org/doi/10.1145/3769821},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 1,488 | Towards Model-based Pricing for Machine Learning in a Data Marketplace | 2019 | SIGMOD | 0.00010612416 |
| 3,705 | Protecting Data Markets from Strategic Buyers | 2022 | SIGMOD | 7.1826873e-05 |
| 3,759 | Dealer: An End-to-End Model Marketplace with Differential Privacy | 2021 | VLDB | 7.14674e-05 |
| 4,106 | Secure Shapley Value for Cross-Silo Federated Learning | 2023 | VLDB | 6.8980966e-05 |
| 4,785 | Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia | 2023 | SIGMOD | 6.5084717e-05 |
| 6,010 | ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs | 2023 | VLDB | 6.0112251e-05 |
| 6,551 | Efficient Sampling Approaches to Shapley Value Approximation | 2023 | SIGMOD | 5.8429222e-05 |
| 11,191 | A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition | 2024 | SIGMOD | 5.093636e-05 |
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