mlsanityData to 14 Oct 2026

Differentially Private Policy Evaluation

arXiv 1603.02010 ↗First posted 7 Mar 2016cs.LG stat.MLcited by 19

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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.

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

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Citing papers per year: 2020: 1; 2021: 5; 2022: 5; 2023: 2; 2024: 2; 2025: 3; 2026: 1.

19 papers.

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  1. 2021Differentially Private Regret Minimization in Episodic Markov Decision Processescited by 23
  2. 2022Differentially Private Reinforcement Learning with Linear Function Approximationcited by 22
  3. 2022Offline Reinforcement Learning with Differential Privacycited by 20
  4. 2023Privately Aligning Language Models with Reinforcement Learningcited by 17
  5. 2021Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processescited by 13
  6. 2021Adaptive Control of Differentially Private Linear Quadratic Systemscited by 7
  7. 2022Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimizationcited by 7
  8. 2021How Private Is Your RL Policy? An Inverse RL Based Analysis Frameworkcited by 5
  9. 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
  10. 2025Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learningcited by 3
  11. 2025On the Sample Complexity of Differentially Private Policy Optimizationcited by 3
  12. 2025Improved Algorithms for Differentially Private Language Model Alignmentcited by 2
7 more
  1. 2021Reinforcement Learning on Encrypted Datacited by 1
  2. 2022Privacy-Preserving Reinforcement Learning Beyond Expectationcited by 1
  3. 2023DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learningcited by 1
  4. 2024Differentially Private Reinforcement Learning with Self-Playcited by 1
  5. 2024Privacy Preserving Reinforcement Learning for Population Processescited by 1
  6. 2026Differential Privacy in the Extensive-Form Bandit Problemcited by 1
  7. 2020Preventing Imitation Learning with Adversarial Policy Ensemblescited by 0