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

Hugging Face Daily Papers Papers
privacy negotiation agents differential-privacy inference-attacks randomized-policies security

Summary

This paper formalizes behavioral privacy leakage in multi-round agentic negotiation and proposes an adaptive stochastic policy that provides differential privacy guarantees while maintaining high negotiation utility.

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.
Original Article
View Cached Full Text

Cached at: 07/20/26, 05:26 PM

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

Source: https://huggingface.co/papers/2607.06815

Abstract

Autonomousnegotiationagentsareincreasinglydeployedinhigh-stakessettingssuchasinsuranceandprocurement.Whilecryptographictechniquesprotectexplicitlydisclosedconstraintvalues,theyfailtoaddressasubtlerthreat:behavioralprivacyleakage,whereanadversaryinfersprivateconstraintsfromobservablenegotiationdynamicssuchasconcessiontrajectories,timing,andconvergencepatterns.Thispaperinvestigatesbehavioraldifferentialprivacyinmulti-roundnegotiationprotocols.Wedesignanadaptivestochasticnegotiationpolicythatjointlyguarantees(varepsilon,δ)-differentialprivacy,almost-sureconvergenceoftheoffersequence(reachingagreementwhenthecounterparty’sreservationvaluepermits),andhighnegotiationutility.Evaluatedon3,000syntheticbilateralnegotiations,ourmechanismreducesadversarialinferenceaccuracyby43-50%whilemaintaininganegotiationsuccessrateandutilityabove90%,demonstratingthatstrongprivacyguaranteescanbeachievedwithoutsignificantlossofperformance.

View arXiv pageView PDFAdd to collection

Get this paper in your agent:

hf papers read 2607\.06815

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2607.06815 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2607.06815 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2607.06815 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

Agentic AI & Crypto: The Need for Privacy in Agentic Trading Markets

Reddit r/ArtificialInteligence

An analysis of how autonomous AI agents are beginning to trade, negotiate, and manage crypto assets 24/7, and why privacy technologies like zero-knowledge proofs are critical to prevent front-running and surveillance in this emerging agentic trading market.

When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

arXiv cs.AI

This paper studies the evaluation of agentic AI systems that repair decision policies when per-state expert action labels are unavailable, using a hotel-pricing simulator with region-level diagnostic feedback. It finds that aggregate alignment can be misleading and proposes evaluating policy repair by closed-loop outcome rather than behavioral distance.