A Cramér–von Mises Approach to Incentivizing Truthful Data Sharing

Alex Clinton, Thomas Zeng, Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy

Keywords: Data sharing, two-sample testing, mechanism design

TL;DR: We incentivize truthful data sharing using a novel technique inspired by two sample testing.

Abstract:

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards.
Prior work has proposed comparing each agent’s data against others’ to promote honesty: when others contribute genuine data, the best way to minimize discrepancy is to do the same. Yet prior implementations of this idea rely on very strong assumptions about the data distribution (e.g. Gaussian), limiting their applicability.
In this work, we develop reward mechanisms based on a novel, two-sample test statistic inspired by the Cramér-von Mises statistic.
Our methods strictly incentivize agents to submit more genuine data, while disincentivizing data fabrication and other types of untruthful reporting.
We establish that truthful reporting constitutes a (possibly approximate) Nash equilibrium in both Bayesian and prior-agnostic settings. We theoretically instantiate our method in two canonical data sharing problems and show that it relaxes key assumptions made by prior work.
Empirically, we demonstrate that our mechanism incentivizes truthful data sharing via simulations and on real-world language and image data.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 13681