Preserving the Privacy of Reward Functions in MDPs through Deception

This paper addresses privacy protection for reward functions in Markov Decision Processes (MDPs) by introducing a deception-based approach that allows agents to hide their true objectives while still achieving optimal performance. The technique is particularly relevant for regulated industries where reward functions encode sensitive business logic, compliance constraints, or proprietary decision-making criteria that organizations want to protect from external observation or reverse-engineering.

This paper addresses privacy protection for reward functions in Markov Decision Processes (MDPs) by introducing a deception-based approach that allows agents to hide their true objectives while still achieving optimal performance. The technique is particularly relevant for regulated industries where reward functions encode sensitive business logic, compliance constraints, or proprietary decision-making criteria that organizations want to protect from external observation or reverse-engineering.

Legal AI must preserve privilege, satisfy ethics rules, and keep client data within a defensible perimeter. QuettaMinds helps law firms and legal departments deploy AI that is structurally compliant, not just policy-compliant.