🤖 AI Summary
Existing autonomous commercial protocols struggle to achieve interoperable, tamper-proof auditing and event temporal verification across heterogeneous domains. This work proposes a verifiable global event timeline architecture that constructs a reproducible, tamper-resistant AI fraud intelligence training pipeline by formalizing event schemas, employing deterministic batching, leveraging Merkle append-only commitments, and anchoring events to blockchain-based timestamps. The approach innovatively integrates cryptographic fraud markers—binding risk labels with anchored evidence—and a data provenance model to establish a verifiable, traceable, AI-ready intelligence layer. Evaluated on a prototype processing 50,000 events, the system constructs Merkle trees in just 47 milliseconds, achieves end-to-end verification in under 0.013 milliseconds, and exhibits logarithmic proof size growth, yielding a 14.4× improvement in verification efficiency over linear scanning.
📝 Abstract
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.