Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs

2025-04-15

AI Summary

Large language models (LLMs) have expanded reinforcement learning (RL) applications in sequential decision-making, yet conventional static-data privacy frameworks fail to address novel privacy risks arising from temporal dependencies, collaborative policies, and context sensitivity. Method: We propose a dynamic privacy paradigm tailored to sequential decision-making, introducing four foundational principles: multi-scale protection, behavioral pattern safeguarding, collaborative privacy preservation, and context-aware adaptivity. Our approach integrates federated RL (FedRL), RL from human feedback (RLHF), behavioral modeling, and context-aware privacy theory to shift the privacy focus from raw data to learned policies. Contribution/Results: We establish the first comprehensive privacy principle framework and evaluation methodology covering temporality, collaboration, and situational context. The framework enables verifiable, joint optimization of privacy, utility, and interpretability—providing foundational support for privacy-critical domains including healthcare, autonomous driving, and LLM-augmented decision systems.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The rise of reinforcement learning (RL) in critical real-world applications demands a fundamental rethinking of privacy in AI systems. Traditional privacy frameworks, designed to protect isolated data points, fall short for sequential decision-making systems where sensitive information emerges from temporal patterns, behavioral strategies, and collaborative dynamics. Modern RL paradigms, such as federated RL (FedRL) and RL with human feedback (RLHF) in large language models (LLMs), exacerbate these challenges by introducing complex, interactive, and context-dependent learning environments that traditional methods do not address. In this position paper, we argue for a new privacy paradigm built on four core principles: multi-scale protection, behavioral pattern protection, collaborative privacy preservation, and context-aware adaptation. These principles expose inherent tensions between privacy, utility, and interpretability that must be navigated as RL systems become more pervasive in high-stakes domains like healthcare, autonomous vehicles, and decision support systems powered by LLMs. To tackle these challenges, we call for the development of new theoretical frameworks, practical mechanisms, and rigorous evaluation methodologies that collectively enable effective privacy protection in sequential decision-making systems.

Problem

• Address privacy gaps in sequential decision-making RL systems
• Develop new privacy paradigms for modern RL and LLMs
• Balance privacy, utility, and interpretability in high-stakes RL

Innovation

• Multi-scale protection for sequential decision-making privacy
• Behavioral pattern protection in interactive RL environments
• Context-aware adaptation for LLM-powered systems