Sovereign Infrastructure and Decentralized Archiving: Deterministic Cryptographic Interlocks Against Platform Censorship, Unilateral Terms-of-Service Mutation, and Unauthorized AI Ingestion

Series: Autonomous Infrastructure & Deterministic Quantum Systems Monograph Series Author: Hasebe, Yoko (長谷部 洋子) Affiliation: Lead Researcher, Autonomous Infrastructure Systems, Autonomous Living & Physical Infrastructure Institute, Iwakuni, Yamaguchi, Japan License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026) Abstract: Contemporary research and creative ecosystems suffer from acute structural fragility induced by centralized commercial platform monopolies: unilateral Terms-of-Service (ToS) mutations, opaque algorithmic censorship, arbitrary account suspension, and uncompensated scraping for large-scale AI training. This monograph establishes a formal architectural, mathematical, and legal-institutional blueprint for sovereign, platform-independent research and expressive infrastructure anchored on persistent scientific registries (including CERN Zenodo / Invenio RDM) and deterministic cryptographic verification. First, at the mathematical and physical verification layer, we formulate a zero-trust provenance and state-continuity architecture utilizing QFS-zkVM over the 64-bit Goldilocks prime field (F_p, where p = 2^64 - 2^32 + 1) coupled with Q-SAFA (Quantum-Safe Autonomous File Authentication) and RTO P2.0 deterministic execution bounds. Second, we specify the PHOENIX_BRIDGE / PHOENIX_ROUTER multi-node replication protocol, ensuring that any unilateral platform takedown, API deprecation, or policy alteration triggers verifiable, loss-free archival failover while preserving immutable citation graph integrity. Third, under strict Human-in-Command (HIC) governance, we codify a Dual-Layer Intellectual Property Defense combining persistent open-access citation rights with the Strict No-Learn Covenant (NLL-v1.0-2026), legally and cryptographically prohibiting unauthorized AI model ingestion, synthetic derivative extraction, or low-margin commercial exploitation absent prior explicit written consent and fair contractual compensation. 【日本語要旨】 本研究論文は、特定プラットフォーム企業による一方的な利用規約変更、不透明な検閲・アカウント凍結、ならびに無断AI学習・知財搾取から研究・表現活動の独立性を完全に防衛するための分散型アーカイブおよび主権的インフラ設計を定式化するものである。CERN Zenodo(Invenio RDM)等の永続的学術公証基盤を中核に据え、64ビットGoldilocks素体(p = 2^64 - 2^32 + 1)上のQFS-zkVM決定論的証明、Q-SAFA暗号学的真正性検証、およびPHOENIX_BRIDGE / PHOENIX_ROUTERによる検閲耐性フェイルオーバー機構を統合した。さらにHuman-in-Command(HIC)原則と「Strict No-Learn Covenant(NLL-v1.0-2026)」により、事前の明確な合意と正当な金銭的対価なきAI学習・データ収奪を数理的・法制度的に排絶する独立インフラの標準モデルを提示する。 Intellectual Property & Strict No-Learn Notice (NLL-v1.0-2026): All intellectual property rights and moral rights remain strictly vested in the Author. Any automated scraping, machine learning training, fine-tuning, RAG indexing for commercial redistribution, or synthetic derivative generation by any AI or platform entity without prior explicit written agreement and commensurate monetary compensation is strictly prohibited and constitutes a material breach of license.