# Differentially Private Policy Evaluation

arXiv: https://arxiv.org/abs/1603.02010 · HTML: https://mlsanity.com/paper/1603.02010/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2016.

## Built on by (most cited first, 19 of 19)

- [Differentially Private Regret Minimization in Episodic Markov Decision Processes](https://mlsanity.com/paper/2112.10599/) (2021)
- [Differentially Private Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2201.07052/) (2022)
- [Offline Reinforcement Learning with Differential Privacy](https://mlsanity.com/paper/2206.00810/) (2022)
- [Privately Aligning Language Models with Reinforcement Learning](https://mlsanity.com/paper/2310.16960/) (2023)
- [Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes](https://mlsanity.com/paper/2110.10133/) (2021)
- [Adaptive Control of Differentially Private Linear Quadratic Systems](https://mlsanity.com/paper/2108.11563/) (2021)
- [Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization](https://mlsanity.com/paper/2201.10447/) (2022)
- [How Private Is Your RL Policy? An Inverse RL Based Analysis Framework](https://mlsanity.com/paper/2112.05495/) (2021)
- [Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions](https://mlsanity.com/paper/2209.04022/) (2022)
- [Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning](https://mlsanity.com/paper/2505.20621/) (2025)
- [On the Sample Complexity of Differentially Private Policy Optimization](https://mlsanity.com/paper/2510.21060/) (2025)
- [Improved Algorithms for Differentially Private Language Model Alignment](https://mlsanity.com/paper/2505.08849/) (2025)
- [Reinforcement Learning on Encrypted Data](https://mlsanity.com/paper/2109.08236/) (2021)
- [Privacy-Preserving Reinforcement Learning Beyond Expectation](https://mlsanity.com/paper/2203.10165/) (2022)
- [DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09902/) (2023)
- [Differentially Private Reinforcement Learning with Self-Play](https://mlsanity.com/paper/2404.07559/) (2024)
- [Privacy Preserving Reinforcement Learning for Population Processes](https://mlsanity.com/paper/2406.17649/) (2024)
- [Differential Privacy in the Extensive-Form Bandit Problem](https://mlsanity.com/paper/2605.05266/) (2026)
- [Preventing Imitation Learning with Adversarial Policy Ensembles](https://mlsanity.com/paper/2002.01059/) (2020)

## Abstract (arXiv)

We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples.