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"

9 / 9 papers shown

Title
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems Mojtaba Taherisadr S. Stavroulakis Salma Elmalaki 29 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 39 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 34 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 31 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 47 3 0 08 Sep 2021
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 38 110 0 07 Nov 2020
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial Amal Feriani Ekram Hossain 40 237 0 06 Nov 2020
Characterizing Attacks on Deep Reinforcement Learning Xinlei Pan Chaowei Xiao Warren He Shuang Yang Jian Peng ... Jinfeng Yi Zijiang Yang Mingyan D. Liu Bo Li D. Song AAML 22 69 0 21 Jul 2019
CAD2RL: Real Single-Image Flight without a Single Real Image Fereshteh Sadeghi Sergey Levine SSL 246 812 0 13 Nov 2016