ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
Linkang Du
Network and Distributed System Security (NDSS) Symposium 2024 · Day 2 · Privacy & Fingerprinting
In the rapidly evolving landscape of artificial intelligence, **Deep Reinforcement Learning (DRL)** has emerged as a transformative paradigm, driving innovation across complex decision-making domains from autonomous systems to critical infrastructure control. However, the direct application of DRL in real-world, safety-critical environments poses significant risks, necessitating the adoption of **offline DRL**. This approach trains models on pre-collected datasets, circumventing hazardous real-world interactions. While fostering research and development, the open-source publication of these valuable datasets introduces a critical challenge: protecting the intellectual property of data owners against misuse, data theft by ex-employees, or the unauthorized creation of pirated Model-as-a-Service platforms.