Summary of No-regret Exploration in Shuffle Private Reinforcement Learning, by Shaojie Bai et al.
No-regret Exploration in Shuffle Private Reinforcement Learning
by Shaojie Bai, Mohammad Sadegh Talebi, Chengcheng Zhao, Peng Cheng, Jiming Chen
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a new trust model for differential privacy (DP) in episodic reinforcement learning (RL), which addresses user privacy concerns in personalized services. The proposed model is stronger than the central model but has a lower privacy cost than the local model, making it suitable for various scenarios. The authors present a generic algorithm for RL under this shuffle model, where a trusted shuffler randomly permutes user data before sending it to the central agent. They also propose a Privatizer mechanism based on a shuffle private binary summation mechanism. The analysis shows that the algorithm achieves near-optimal regret bounds comparable to those of the centralized model and outperforms the local model in terms of privacy cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new way to protect user data in personalized services like recommendations or games. They want to make sure that users’ private information isn’t shared without their permission. The authors introduce a new method called the “shuffle” model, which is stronger than some existing methods but doesn’t require as much extra work. They also develop an algorithm that uses this shuffle model and test it with a Privatizer mechanism. This algorithm helps keep user data safe while still allowing for good results in personalized services. |
Keywords
* Artificial intelligence * Reinforcement learning
