arXiv:2607.19436cs.CRcs.AI2026-07

为自主商业设计可验证的时间线与防欺诈系统,提升跨域交易可信度。

Building Trust in Autonomous Commerce: A Verifiable Global Event Timeline and AI-Ready Fraud Intelligence Layer

  • 用统一事件格式与确定性批处理实现无时钟依赖的事件排序
  • 构建支持快速验证的梅克尔树结构,5万条事件验证耗时不足0.013毫秒
  • 提供可追溯的欺诈标记与训练数据溯源,适合风控与AI训练场景

自主商业协议如AP2和ACP虽能保障代理发起交易的安全性,但缺乏跨异构领域间可互操作、抗篡改的审计能力及事件的可验证时间排序。本文提出一种可验证的全局事件时间线,由四大核心组件构成:标准化事件模式确保确定性序列化,确定性批量生成实现无需同步时钟的可复现排序,基于梅克尔树的追加式承诺支持对数复杂度的包含证明,区块链锚定建立防篡改的时间主干。在此基础上,提出密码学签名的欺诈标记,通过不可伪造的来源链将风险标签与锚定证据绑定,并构建可复现、防篡改的模型训练数据谱系。原型实测表明:梅克尔树构建5万条事件仅需47毫秒;端到端验证时间始终低于0.013毫秒,与批次大小无关;包含证明大小从1,000事件时的320字节增至5万事件时的512字节,呈对数增长;在5万事件下,梅克尔验证比线性扫描快14.4倍。

原文摘要 · Abstract (English)

Agentic commerce protocols such as AP2 and ACP define mechanisms for secure agent-initiated transactions but do not provide interoperable, tamper-evident auditability or verifiable temporal ordering of events across heterogeneous domains. This paper addresses these gaps by proposing a verifiable global event timeline for agentic commerce, constructed from four core components: canonical event schemas that enforce deterministic serialization, deterministic batch formation ensuring reproducible ordering without reliance on synchronized clocks, Merkle-based append-only commitments providing logarithmic-cost inclusion proofs, and blockchain anchoring establishing a tamper-evident temporal backbone. Building on this infrastructure, we introduce a cryptographically signed fraud marker that binds risk labels to anchored evidence through an unforgeable provenance chain, and a dataset lineage model enabling reproducible, tamper-evident AI training pipelines. Empirical results from a prototype implementation demonstrate: Merkle tree construction processes 50,000 events in 47 milliseconds; end-to-end verification completes in under 0.013 milliseconds regardless of batch size; inclusion proof sizes grow logarithmically from 320 bytes at 1,000 events to 512 bytes at 50,000 events; and Merkle-based verification outperforms linear scan by 14.4x at 50,000 events.

自主商业事件时间线防欺诈梅克尔树

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