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: 7417
Venue: SIGMOD
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,107 | 29.69%
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
1,298	Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms	2019	VLDB	0.00012758104
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
4,138	Protecting Data Markets from Strategic Buyers	2022	SIGMOD	6.4175758e-05
4,753	Secure Shapley Value for Cross-Silo Federated Learning	2023	VLDB	5.9469115e-05
4,863	Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia	2023	SIGMOD	5.8697471e-05
6,257	ZKSQL: Verifiable and Efficient Query Evaluation with Zero-Knowledge Proofs	2023	VLDB	5.1366858e-05
7,380	Efficient Sampling Approaches to Shapley Value Approximation	2023	SIGMOD	4.746272e-05
10,978	A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition	2024	SIGMOD	4.1945683e-05

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