Sovereign Zero-Knowledge Governance for National Health Insurance and Preventive Medicine AI: Prohibiting Raw Epidemiological Weight Contagion, Algorithmic Worker Profiling, and Central Data Aggregation Across Occupational Health Funds

Abstract

Public health ministries are increasingly deploying centralized artificial intelligence (AI) systems to aggregate subscriber health records and prescribe preventive medical interventions across occupational and municipal insurance funds. Specifically, Japan's Ministry of Health, Labour and Welfare (MHLW) has initiated a 5.0 billion JPY budget request for FY2027 (targeting FY2028 pilot trials and FY2030 full-scale operation) to construct a centralized AI proposal platform spanning corporate Health Insurance Societies (Kenpo Kumiai), the Japan Health Insurance Association (Kyokai Kenpo) covering small-to-medium enterprises, and Municipal National Health Insurance (Kokumin Kenko Hoken). Under this paradigm, insurers input subscriber attributes (e.g., night-shift schedules, chronic lifestyle disease prevalence, specific health checkup Tokutei Kenshin biomarkers, and historical intervention outcomes) into a central AI engine that outputs targeted behavioral directives. Building upon our foundational proofs of weight-space intellectual property and privacy contagion (CERN Zenodo Record 23360530), Human-in-Command (HIC) governance (Record 23375986), and zero-knowledge clinical telemetry under Super-RTO (Record 23377123), this monograph establishes the formal mathematical, cryptographic, hardware, and statutory prohibition against unattested centralized health-insurance AI architectures. We prove four interlocking propositions: (1) Irreversible Epidemiological Weight Contagion: Pooling occupational and biomarker tensors into centralized neural weights W induces non-diagonal Hessian cross-covariance coupling, enabling membership inference and corporate shift-morbidity reconstruction that cannot be excised via post-hoc orthogonal unlearning; (2) Anti-Profiling & Counterfactual Non-Discrimination: We formulate a strict mathematical prohibition against algorithmic worker scoring, enforcing zero Kullback-Leibler divergence across protected labor attributes (D_KL = 0) and mandatory dual cryptographic sign-off by licensed occupational physicians (Sangyoui) and subscribers under HIC; (3) Sovereign Zero-Knowledge Federated Verification (QFS-zkVM): Using recursive STARK proofs over the 64-bit Goldilocks prime field (F_p, p = 2^64 - 2^32 + 1), local Kenpo vaults prove intervention efficacy in zero knowledge without exporting raw subscriber attributes (I_exfil = 0); and (4) Hardware No-Learn Interlocks (PHOENIX_ROUTER): Physical Pockels shutters sever compute buses in < 7.0 ns upon detecting unauthorized gradient updates, enforcing compliance with Articles 13 and 25 of the Constitution of Japan, Special Care-Required Personal Information (Yohairyo Kojin Joho) mandates under Japan's APPI, and trustee fiduciary duties. License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026).