Hydro-DPFLQB: A Differentially Private Federated Learning-Assisted Quantum-Safe Blockchain Method for the Green Hydrogen Supply Chain
Hydro-DPFLQB: グリーン水素サプライチェーンのための差分プライバシー連合学習支援量子安全ブロックチェーン手法 (AI 翻訳)
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🤖 gxceed AI 要約
日本語
本論文は、グリーン水素サプライチェーンにおける大規模IoTデータのプライバシー保護とリアルタイムトレーサビリティを実現するため、差分プライバシー連合学習と量子安全ブロックチェーンを組み合わせたフレームワークを提案する。ノードはデータを共有せずにローカルモデルを訓練し、ロバストな中央値プーリングと適応ノイズスケーリングにより98%の精度を維持しつつプライバシー予算の増加を最小限に抑える。ML-KEMハイブリッド暗号による量子攻撃耐性も備え、IPFSにデータを安全に保存する。シミュレーション実験により、提案手法のセキュリティと実用性を実証した。
English
This paper proposes a framework combining differentially private federated learning and quantum-safe blockchain for privacy-preserving real-time traceability of IoT data in the green hydrogen supply chain. Nodes train local models without sharing raw data, achieving 98% accuracy with minimal privacy budget increase (Δε<0.003). ML-KEM-based hybrid encryption protects against quantum attacks, and data is stored securely in IPFS. Simulations demonstrate security and feasibility.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は水素基本戦略を掲げ、グリーン水素のサプライチェーン構築を推進している。本論文は、IoTデータのプライバシー保護とセキュアな共有を実現する技術を提供し、日本の水素社会実装におけるデータ信頼性向上に貢献する可能性がある。
In the global GX context
Globally, hydrogen supply chains face challenges in data trust and security. This work introduces a novel approach using federated learning and blockchain to ensure privacy and integrity, applicable to emerging hydrogen infrastructure and relevant for international standards on green hydrogen traceability and data governance.
👥 読者別の含意
🔬研究者:This paper provides a novel architecture integrating differential privacy, federated learning, and post-quantum blockchain for hydrogen supply chain, offering a foundation for future research on secure IoT in energy systems.
🏢実務担当者:Corporate sustainability and supply chain teams can adopt this framework to ensure secure, privacy-preserving traceability of green hydrogen, enhancing trust among stakeholders and compliance with data protection regulations.
🏛政策担当者:Regulators can note this technology for setting data security standards in hydrogen infrastructure, especially for cross-border hydrogen trade and international certification schemes.
📄 Abstract（原文）
Recently, the conversion of excess electricity generated from renewable sources into hydrogen has emerged as a promising alternative that significantly contributes to carbonneutral targets by enabling both the storage and distribution of energy to various sectors. However, the privacy-friendly realtime traceability of large volumes of IoT data in the hydrogen supply chain, as well as the trust issue between stakeholders, are well-known challenges in the literature. This work presents a secure post-quantum blockchain-based framework enhanced by privacy-preserving federated learning. In the proposed framework, nodes in the supply chain train their local models without sharing raw data. Robust median pooling and adaptive noise scaling maintain accuracy up to 98 %, while the privacy budget increases by only $\Delta \varepsilon<0.003$. Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM)-based hybrid encryption protects client models against quantum attacks, while data is securely stored in IPFS. In this study, the accuracy, privacy, and efficiency of the approach are evaluated by simulating the processes of different actors in the supply chain on a scenario-byscenario basis. The experimental results demonstrate the security and feasibility of the system by combining secure data sharing with high model performance of the proposed architecture.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/acdsa67686.2026.11468128first seen 2026-05-15 19:39:00
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。