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[2506.07272] A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing
Summary
This paper introduces a novel approach using the Cramér-von Mises statistic to create incentive mechanisms that promote truthful data sharing in marketplaces, addressing the manipulation risks of previous methods.
Why It Matters
As data sharing becomes increasingly critical in various sectors, ensuring the integrity of submitted data is vital. This research offers a robust solution to incentivize honesty, potentially transforming how data marketplaces operate and enhancing data quality.
Key Takeaways
- Introduces a Cramér-von Mises-based mechanism for data sharing.
- Addresses vulnerabilities in existing incentive schemes that reward quantity over quality.
- Establishes truthful reporting as a Nash equilibrium in various settings.
- Demonstrates empirical effectiveness through simulations and real-world data.
- Relaxes strong assumptions made by prior research on data distribution.
Computer Science > Machine Learning arXiv:2506.07272 (cs) [Submitted on 8 Jun 2025 (v1), last revised 15 Feb 2026 (this version, v2)] Title:A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing Authors:Alex Clinton, Thomas Zeng, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy View a PDF of the paper titled A Cram\'er-von Mises Approach to Incentivizing Truthful Data Sharing, by Alex Clinton and 4 other authors View PDF HTML (experimental) 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 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 pr...
