Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies

arXiv:2607.06815 · cs.CR · Submitted 2026-07-07 · Read on arXiv

Barkha Rani

cs.CR

Submitted: 2026-07-07

Comments: 3 figures. Accepted at the AI4TCI Workshop (AI for Secure and Trustworthy Critical Infrastructure Systems), co-located with ARES 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Differential Privacy — 29× in this paper · explained in 4 episodes
Trace — 20× in this paper · explained in 2 episodes
Accuracy — 18× in this paper · explained in 1 episode
Ablation — 3× in this paper · explained in 1 episode
Core — 3× in this paper · explained in 2 episodes
Convergence Guarantees — 2× in this paper · explained in 3 episodes
Differentially Private — 2× in this paper · explained in 3 episodes
Federated Learning — 2× in this paper · explained in 15 episodes
Meta-learning — 2× in this paper · explained in 2 episodes
Multi-Agent System — 2× in this paper · explained in 6 episodes
Theoretical Guarantees — 2× in this paper · explained in 2 episodes
Ablation study — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Computational overhead — 1× in this paper · explained in 2 episodes
Differential Privacy (DP) — 1× in this paper · explained in 7 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Multi-Agent Framework — 1× in this paper · explained in 3 episodes
Robustness — 1× in this paper · explained in 5 episodes

Sources

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