Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
Apple ML Research
Researchers developed an adaptive stochastic policy for autonomous negotiation agents that protects behavioral privacy by preventing adversaries from inferring private constraints from observable negotiation dynamics like concession patterns and timing. The mechanism achieved a 43-50% reduction in adversarial inference accuracy while maintaining negotiation success rates and utility above 90% across 3,000 synthetic bilateral negotiations. This approach enables negotiation agents to operate with differential privacy guarantees without substantially sacrificing negotiation performance or deal completion rates.
Why it matters
This paper was accepted at the AI4TCI (Workshop on AI for Secure and Trustworthy Critical Infrastructure Systems) Workshop at the International Conference on Availability, Reliability and Security (ARES) 2026. Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address a subtler threat: behavioral privacy leakage, where an adversary infers private constraints from observable negotiation dynamics such as concession trajectories, timing, and…
