# Truthful Data Acquisition via Peer Prediction

arXiv: https://arxiv.org/abs/2006.03992 · HTML: https://mlsanity.com/paper/2006.03992/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 14 of 14)

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- [Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution](https://mlsanity.com/paper/2407.15881/) (2024)
- [Equilibrium of Data Markets with Externality](https://mlsanity.com/paper/2302.08012/) (2023)
- [Truthful Generalized Linear Models](https://mlsanity.com/paper/2209.07815/) (2022)
- [KAIROS: Scalable Model-Agnostic Data Valuation](https://mlsanity.com/paper/2506.23799/) (2025)
- [Data Overvaluation Attack and Truthful Data Valuation in Federated Learning](https://mlsanity.com/paper/2502.00494/) (2025)
- [Data Sharing with a Generative AI Competitor](https://mlsanity.com/paper/2505.12386/) (2025)
- [A Cram'er-von Mises Approach to Incentivizing Truthful Data Sharing](https://mlsanity.com/paper/2506.07272/) (2025)
- [Multi-agent Adaptive Mechanism Design](https://mlsanity.com/paper/2512.21794/) (2025)
- [Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning](https://mlsanity.com/paper/2605.11889/) (2026)
- [When Individually Calibrated Models Become Collectively Miscalibrated](https://mlsanity.com/paper/2605.18858/) (2026)
- [DaDaDa: A Dataset for Data Pricing in Data Marketplaces](https://mlsanity.com/paper/2607.08785/) (2026)

## Abstract (arXiv)

We consider the problem of purchasing data for machine learning or statistical estimation. The data analyst has a budget to purchase datasets from multiple data providers. She does not have any test data that can be used to evaluate the collected data and can assign payments to data providers solely based on the collected datasets. We consider the problem in the standard Bayesian paradigm and in two settings: (1) data are only collected once; (2) data are collected repeatedly and each day's data are drawn independently from the same distribution. For both settings, our mechanisms guarantee that truthfully reporting one's dataset is always an equilibrium by adopting techniques from peer prediction: pay each provider the mutual information between his reported data and other providers' reported data. Depending on the data distribution, the mechanisms can also discourage misreports that would lead to inaccurate predictions. Our mechanisms also guarantee individual rationality and budget feasibility for certain underlying distributions in the first setting and for all distributions in the second setting.