A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing (2506.07272v1)

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 Cram\'er-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.

• The paper introduces a mechanism leveraging the CvM statistic to establish truthful reporting as a Nash equilibrium in data-sharing settings.
• The methodology uses a loss function based on a two-sample test to disincentivize data fabrication while encouraging authentic submissions.
• Empirical evaluations on simulated and real-world datasets demonstrate the approach's scalability and robustness across diverse data types.

A CvM-Based Approach to Incentivizing Truthful Data Sharing

The paper presents a novel mechanism for incentivizing agents in data marketplaces and sharing consortia to contribute genuine data. Instead of depending on the traditional and often unreliable metric of data quantity, the authors introduce a mechanism that leverages a two-sample test based on the Cramér-von Mises (CvM) statistic. This method is designed to encourage truthful data submissions by making genuine reporting an equilibrium in both Bayesian and prior-agnostic settings.

Mechanism and Theoretical Insights

The authors propose a mechanism that calculates a loss (analogous to negative rewards) using a two-sample test statistic. This loss effectively disincentivizes data fabrication and untruthful reporting by examining the discrepancy between an agent's data and that of others. The CvM-inspired approach relaxes many of the stringent assumptions required by prior work, particularly regarding knowledge of data distributions (e.g., Gaussian assumptions). Instead, it can effectively handle a broader range of data types and distributions.

Key theoretical insights include the establishment of truthful reporting as a Nash equilibrium. This is accompanied by proof that submitting additional genuine data improves an agent's outcome, addressing a key challenge in designing incentives: the reward's sensitivity to data quantity and quality. Additionally, the mechanism's adaptability is showcased across three canonical problems in data sharing, indicating its robustness.

Practical Applicability and Empirical Validation

The paper underlines the importance of applying these theoretical findings in practical scenarios by demonstrating the mechanism's utility through both simulated and real-world datasets, including language and image data. The approach's scalability and adaptability to a wide array of data types make it a significant contribution to the current state of incentivizing data contributions.

The empirical results confirm that compared to traditional methods, this CvM-based test successfully penalizes fabricated data while rewarding authentic submissions. These findings are further supported by using modern data paradigms, such as text and image data, which illustrate the mechanism's potential in varied applications.

Implications and Future Directions

The implications of this research are manifold. Practically, the proposed mechanism can enhance the reliability and efficiency of data marketplaces and consortia by safeguarding against data manipulation. Theoretically, it pushes the boundary of incentive-compatible design by broadening the applicability beyond narrow distributional assumptions.

Future research could extend this approach by exploring its integration with more complex data modalities and its adaptability in dynamic data environments. Additionally, further exploration into computational efficiencies could bolster its application in real-time settings, laying the groundwork for more efficient, secure, and incentive-compatible data exchange systems across industries.