Full-Lineage Cryptographic Data Provenance: Deterministic Zero-Knowledge Auditing to Trace Model Outputs to Unlicensed Training Works and Prove Unauthorized Exploitation(データ来歴[プロヴェナンス]の完全追跡:モデル内の出力から学習元の著作物を特定し、無断利用を立証するための暗号学的監査手法)

This monograph establishes a rigorous, mathematically verifiable cryptographic auditing framework for full-lineage data provenance, enabling rights holders and auditors to deterministically trace foundation and generative model outputs back to specific unlicensed copyrighted works ingested during training. Addressing systemic intellectual property expropriation where AI platforms scrape and monetize creative and scholarly assets without prior explicit consent or fair financial compensation, we formulate a zero-knowledge provenance architecture built upon QFS-zkVM operating over the 64-bit Goldilocks prime field Fₚ (p = 2⁶⁴ - 2³² + 1). By cryptographically binding corpus Merkle commitments, gradient update trajectories, and output embedding influence signatures under RTO P2.0 and Q-SAFA attestation protocols, the framework proves unauthorized training-set membership and derivative weight extraction without exposing proprietary audit queries or underlying protected works. At the physical and routing layers, PHOENIX_BRIDGE and PHOENIX_ROUTER enforce deterministic pre-inference hardware interlocks under Human-in-Command [HIC] governance, blocking execution of model weights lacking verifiable licensing and royalty-settlement receipts. Furthermore, we map cryptographic audit non-compliance directly onto statutory and fiduciary liability regimes, synthesizing pre-deployment risk gatekeeping modeled on SEC Rule 15c3-5 with board-level oversight liability under Delaware Caremark standards. Any model output exhibiting non-zero uncompensated cryptographic lineage constitutes actionable, self-authenticating forensic evidence of willful infringement. 本研究は、AIモデルの出力から学習元の著作物を一意に特定し、事前の明確な合意および正当な金銭的対価なき無断学習・知財搾取を数学的・法的に立証するための完全データ来歴(プロヴェナンス)暗号監査基盤を定式化する。64ビットGoldilocks素体上のQFS-zkVM、RTO P2.0、およびQ-SAFA証明系を統合し、モデルの重み更新履歴と出力トークン・埋め込み空間の影響度署名から未許諾データの混入を決定論的に検証する。さらにPHOENIX_BRIDGEおよびPHOENIX_ROUTERとHuman-in-Command [HIC] 統治機構により、正当なライセンス証跡を欠くモデルの推論・流通を物理層で遮断するとともに、SEC規則15c3-5型事前ゲートキーピングおよびデラウェア州会社法Caremark基準に基づく経営陣の監視義務違反として法的責任を確立する。 Notice of Covenant: Distributed under Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026). Any automated scraping, machine learning ingestion, weight training, fine-tuning, synthetic distillation, or commercial platform exploitation without prior written agreement and explicit financial compensation is strictly prohibited.