Multi-Agent Nash Q-Learning for Node Security in Personal Privacy

The balance between individuals’ interest in protecting their private information and the interests of other entities (other individuals, confidants, Internet companies, corporations, and government agencies) has been disrupted in the age of ICTs (Information and Communication Technologies). This paper presents a multi-agent learning model based on Nash Q-learning that simulates the interaction between two competing agents—a defender (a private person) and an attacker (a Large ICT company, such as Google or Facebook)—operating in a basic component of privacy nodes with dynamic states (Safe, Attacked, Isolated). Recent work has explored Nash Q-learning in adversarial cybersecurity-for-deception contexts, demonstrating convergence properties in attacker-defender scenarios. The model enables dynamic learning of optimal defense and attack strategies while accounting for the opponent’s behavior. Additionally, the paper addresses the challenges of partial observability and limited inter-agent communication, aligning with recent advances that combine graph-attention with mean-field MARL to improve scalability and decision-making under partial information. We further integrate deep learning components, including attention weighting for critical privacy components—drawing on methods such as AERIAL, which applies attention-based recurrence to handle stochastic observability in multi-agent settings. A simulation involving ten nodes demonstrates the algorithm’s functionality and highlights potential directions for future research.