代理協商中的行為隱私洩漏：透過隨機化策略形式化與緩解推斷攻擊

摘要

自主談判代理人逐漸被部署於保險與採購等高風險場景中。雖然密碼學技術能保護明確揭露的約束值，卻未能應對一項更微妙的威脅：行為隱私洩漏——攻擊者可從可觀察的談判動態（如讓步軌跡、時機與收斂模式）推斷出私有約束。本文探討多輪談判協議中的行為差分隱私。我們設計了一種自適應隨機談判策略，可同時保證 (ε, δ)-差分隱私、要價序列的幾乎必然收斂（在對手保留價值允許的情況下達成協議），以及高度的談判實用性。在 3,000 場合成雙邊談判的評估中，我們的機制將攻擊者的推斷準確度降低了 43-50%，同時維持超過 90% 的談判成功率與實用性，證明了在不顯著損失效能的前提下，能夠實現強大的隱私保障。

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.