How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

24 April 2019

Papers citing "How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning"

8 / 8 papers shown

Title
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems Mojtaba Taherisadr S. Stavroulakis Salma Elmalaki 23 15 0 07 Mar 2023
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning Qing Fu Tenghai Qiu Zhiqiang Pu Jianqiang Yi Wanmai Yuan 26 2 0 05 Aug 2022
A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions Chamara Sandeepa Bartlomiej Siniarski N. Kourtellis Shen Wang Madhusanka Liyanage 29 21 0 08 Mar 2022
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework Kritika Prakash Fiza Husain P. Paruchuri Sujit Gujar OffRL 18 11 0 10 Dec 2021
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning Maziar Gomrokchi Susan Amin Hossein Aboutalebi Alexander Wong Doina Precup MIACV AAML 39 3 0 08 Sep 2021
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 33 109 0 07 Nov 2020
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial Amal Feriani E. Hossain 35 236 0 06 Nov 2020
CAD2RL: Real Single-Image Flight without a Single Real Image Fereshteh Sadeghi Sergey Levine SSL 229 809 0 13 Nov 2016