Agentic AI · AI Inference · Apple Machine Learning
Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
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Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies.
Key facts
- Evaluated on 3,000 synthetic bilateral negotiations, their mechanism reduces adversarial inference accuracy by 43–50% while maintaining a negotiation success rate and utility above 90%, demonstrating
- 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
- Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
- Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement
Summary
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. Evaluated on 3,000 synthetic bilateral negotiations, their mechanism reduces adversarial inference accuracy by 43–50% while maintaining a negotiation success rate and utility above 90%, demonstrating that strong privacy guarantees can be achieved without significant loss of performance.