Reliable and Private Utility Signaling for Data Markets

TL;DR

This paper introduces a privacy-preserving and reliable signaling mechanism for data markets, utilizing secure multi-party computation to improve data trading decisions without compromising privacy.

Contribution

It develops a non-TCP signaling protocol that ensures privacy and reliability, incorporating MPC-based verification and an optimized KNN-Shapley method for fair data valuation.

Findings

The proposed protocol prevents suboptimal decision-making in data trading.

Experiments show the approach is efficient and practical.

The MPC-based scheme ensures input reliability and robustness.

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.