Reliable and Private Utility Signaling for Data Markets
Li Peng, Jiayao Zhang, Yihang Wu, Weiran Liu, Jinfei Liu, Zheng Yan, Kui Ren, Lei Zhang, Lin Qu
TL;DR
This work tackles the problem of signaling data utility in data markets without a trusted broker, aiming to deliver private yet reliable signals to guide pricing and purchases. It introduces a non-TCP signaling mechanism, $\\Pi_{\\mathcal{M}}$, built on maliciously secure MPC and a hash-verification layer to ensure input integrity (AoI) and computation reliability (RoC). The authors also design an MPC-based KNN-Shapley method for fair utility allocation among multiple sellers and provide extensive experiments using MP-SPDZ to validate efficiency and practicality, including hashing variants and KNN-Shapley optimizations. The approach enables informed data trading with non-negative buyer payoff and improved social welfare, while offering tunable trade-offs between rigorous guarantees and performance. Overall, the paper advances private, verifiable signaling and fair data valuation in decentralized data marketplaces.
Abstract
The explosive growth of data has highlighted its critical role in driving economic growth through data marketplaces, which enable extensive data sharing and access to high-quality datasets. To support effective trading, signaling mechanisms provide participants with information about data products before transactions, enabling informed decisions and facilitating trading. However, due to the inherent free-duplication nature of data, commonly practiced signaling methods face a dilemma between privacy and reliability, undermining the effectiveness of signals in guiding decision-making. To address this, this paper explores the benefits and develops a non-TCP-based construction for a desirable signaling mechanism that simultaneously ensures privacy and reliability. We begin by formally defining the desirable utility signaling mechanism and proving its ability to prevent suboptimal decisions for both participants and facilitate informed data trading. To design a protocol to realize its functionality, we propose leveraging maliciously secure multi-party computation (MPC) to ensure the privacy and robustness of signal computation and introduce an MPC-based hash verification scheme to ensure input reliability. In multi-seller scenarios requiring fair data valuation, we further explore the design and optimization of the MPC-based KNN-Shapley method with improved efficiency. Rigorous experiments demonstrate the efficiency and practicality of our approach.