Privacy-Preserving Policy Iteration for Decentralized POMDPs


  • Feng Wu University of Science and Technology of China
  • Shlomo Zilberstein University of Massachusetts Amherst
  • Xiaoping Chen University of Science and Technology of China



Decentralized POMDPs, Privacy-Preserving Planning


We propose the first privacy-preserving approach to address the privacy issues that arise in multi-agent planning problems modeled as a Dec-POMDP. Our solution is a distributed message-passing algorithm based on trials, where the agents' policies are optimized using the cross-entropy method. In our algorithm, the agents' private information is protected using a public-key homomorphic cryptosystem. We prove the correctness of our algorithm and analyze its complexity in terms of message passing and encryption/decryption operations. Furthermore, we analyze several privacy aspects of our algorithm and show that it can preserve the agent privacy of non-neighbors, model privacy, and decision privacy. Our experimental results on several common Dec-POMDP benchmark problems confirm the effectiveness of our approach.




How to Cite

Wu, F., Zilberstein, S., & Chen, X. (2018). Privacy-Preserving Policy Iteration for Decentralized POMDPs. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



AAAI Technical Track: Multiagent Systems