代理式谈判中的行为隐私泄露问题：通过随机化策略来规范并减轻推理攻击
Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
摘要
自主谈判代理被越来越多地应用于保险和采购等高风险领域。虽然加密技术能够保护那些明确公开的约束条件，但它们无法应对一种更隐蔽的威胁：行为隐私泄露问题——即对手通过观察谈判过程中的各种行为特征，如让步轨迹、时间安排以及协商模式，从而推断出私密的约束条件。本文研究了多轮谈判协议中的行为差异隐私问题。我们设计了一种自适应随机谈判策略，该策略能够同时确保(ε, δ)差异隐私、报价序列的几乎必然收敛性（当对方的心理预期值允许时即可达成协议），以及较高的谈判效率。在3,000次模拟双边谈判中测试表明，该机制可将对手的推断准确性降低43-50%，同时保持90%以上的谈判成功率和效率，从而证明无需大幅牺牲性能就能实现可靠的隐私保护。
English Abstract
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 convergence patterns. This paper investigates behavioral differential privacy in multi-round negotiation protocols. We design an adaptive stochastic negotiation policy that jointly guarantees (varepsilon, δ)-differential privacy, almost-sure convergence of the offer sequence (reaching agreement when the counterparty's reservation value permits), and high negotiation utility. Evaluated on 3,000 synthetic bilateral negotiations, our 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.