代理谈判中的行为隐私泄露：通过随机化策略形式化与缓解推断攻击

摘要

自主谈判代理正越来越多地部署在保险、采购等高风险场景中。虽然密码学技术能够保护显式披露的约束值，但未能应对一种更隐蔽的威胁：行为隐私泄露。在此类攻击中，对手可通过观察让步轨迹、时机及收敛模式等可感知的谈判动态，推断出私有约束条件。本文研究了多轮谈判协议中的行为差分隐私问题。我们设计了一种自适应随机谈判策略，该策略能同时保证(ε, δ)-差分隐私、报价序列的几乎必然收敛性（当对手保留价值允许时达成协议）以及高谈判效用。在3000轮合成双边谈判的评估中，我们的机制将对手推理准确率降低了43-50%，同时保持了超过90%的谈判成功率和效用，证明强隐私保障可以在不显著牺牲性能的情况下实现。

English

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.