• Corpus ID: 239998785

Local Differential Privacy for Regret Minimization in Reinforcement Learning

@inproceedings{Garcelon2020LocalDP,
  title={Local Differential Privacy for Regret Minimization in Reinforcement Learning},
  author={Evrard Garcelon and Vianney Perchet and Ciara Pike-Burke and Matteo Pirotta},
  year={2020}
}
Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitive information that needs to be protected from third parties. Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring information to be obfuscated on the user side. We formulate this notion of privacy for RL by leveraging the local differential privacy (LDP… 

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References

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