Reliable and Private Utility Signaling for Data Markets

2025-11-11

AI Summary

In data markets, existing signaling mechanisms struggle to simultaneously ensure privacy preservation and evaluation reliability, leading to suboptimal trading decisions. To address this, we propose the first privacy-reliability co-optimized utility signaling framework. Our method introduces a hash-verification mechanism built upon maliciously secure multi-party computation (MPC) to guarantee input data authenticity; adapts the KNN-Shapley algorithm for multi-seller settings to enable fair and efficient value quantification; and designs a non-TCP-dependent distributed signaling protocol that ensures privacy, integrity, and scalability. Experimental results demonstrate that our framework significantly reduces the risk of suboptimal decisions while maintaining high efficiency and practicality in large-scale, multi-party environments.

Technology Category

• Machine Learning: Privacy
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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.

Problem

• Addresses privacy-reliability dilemma in data market signaling mechanisms
• Develops secure multiparty computation for private robust signal computation
• Enables fair data valuation in multi-seller scenarios using optimized methods

Innovation

• Non-TCP-based signaling mechanism for privacy
• MPC-based hash verification for input reliability
• Optimized MPC-based KNN-Shapley for fair valuation