On the Fairness of Quality-based Data Markets

TL;DR

This paper introduces a fair data market model that incorporates data quality into pricing, ensuring fairness and preventing cheating through a verification mechanism and trusted third parties.

Contribution

It proposes a novel quality-driven data pricing strategy and a fairness assurance mechanism, enhancing fairness and integrity in data markets.

Findings

The pricing strategy assigns reasonable prices based on data quality.

The fairness mechanism effectively prevents cheating and ensures proper charges.

Experimental results confirm the effectiveness of the proposed system.

Abstract

For data pricing, data quality is a factor that must be considered. To keep the fairness of data market from the aspect of data quality, we proposed a fair data market that considers data quality while pricing. To ensure fairness, we first design a quality-driven data pricing strategy. Then based on the strategy, a fairness assurance mechanism for quality-driven data marketplace is proposed. In this mechanism, we ensure that savvy consumers cannot cheat the system and users can verify each consumption with Trusted Third Party (TTP) that they are charged properly. Based on this mechanism, we develop a fair quality-driven data market system. Extensive experiments are performed to verify the effectiveness of proposed techniques. Experimental results show that our quality-driven data pricing strategy could assign a reasonable price to the data according to data quality and the fairness assurance mechanism could effectively protect quality-driven data pricing from potential cheating.