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)

Paper ID: 7418
Venue: SIGMOD
Year: 2026
Pagerank: 5.1725247e-05
Overall Rank: 10,107 | 29.76%
DOI: 10.1145/3769821

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Rank	Citing Paper	Year	Venue	Pagerank

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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
1,124	Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms	2019	VLDB	0.00012165727
1,483	Towards Model-based Pricing for Machine Learning in a Data Marketplace	2019	SIGMOD	0.00010694952
3,724	Dealer: An End-to-End Model Marketplace with Differential Privacy	2021	VLDB	7.2364245e-05
3,815	Protecting Data Markets from Strategic Buyers	2022	SIGMOD	7.1586065e-05
4,228	Secure Shapley Value for Cross-Silo Federated Learning	2023	VLDB	6.8829966e-05
4,751	Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia	2023	SIGMOD	6.5980479e-05
5,922	ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs	2023	VLDB	6.1043252e-05
7,081	Efficient Sampling Approaches to Shapley Value Approximation	2023	SIGMOD	5.7612572e-05
10,981	A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition	2024	SIGMOD	5.1725247e-05

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