Summary of Privacy Preserving Reinforcement Learning For Population Processes, by Samuel Yang-zhao et al.
Privacy Preserving Reinforcement Learning for Population Processes
by Samuel Yang-Zhao, Kee Siong Ng
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper presents a solution to protect individual data privacy in Reinforcement Learning (RL) algorithms operating over population processes, such as controlling epidemics. The proposed approach uses Differential Privacy (DP) mechanisms to privatize the state and reward signal at each time step, ensuring that an individual’s data remains private even when collected across multiple interactions. The authors provide a meta algorithm that can be applied to any RL algorithm to achieve differential privacy. Experimental results on a simulated epidemic control problem demonstrate that reasonable privacy-utility trade-offs are possible for differentially private RL algorithms in population processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about keeping people’s information safe when we use machines to make decisions based on big groups of people, like controlling the spread of diseases. We want to keep each person’s data secret even if we collect it many times. The authors came up with a way to do this using a concept called Differential Privacy. They also created a system that can be used with any machine learning algorithm to make sure our decisions are private and fair. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
