A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917275046510592 |
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| author | Clinton, Alex Zeng, Thomas Chen, Yiding Zhu, Xiaojin Kandasamy, Kirthevasan |
| author_facet | Clinton, Alex Zeng, Thomas Chen, Yiding Zhu, Xiaojin Kandasamy, Kirthevasan |
| contents | 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 prior-agnostic settings. We theoretically instantiate our method in three 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing Clinton, Alex Zeng, Thomas Chen, Yiding Zhu, Xiaojin Kandasamy, Kirthevasan Machine Learning 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 prior-agnostic settings. We theoretically instantiate our method in three 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. |
| title | A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.07272 |