Privacy-Preserving Cross-Chain Federated Blockchain for Carbon-Credit Management
カーボンクレジット管理のためのプライバシー保護型クロスチェーンフェデレーテッドブロックチェーン (AI 翻訳)
C. Rao, Praveen Kumar Naidu Rayanki, Polepalli Rajeev Meenon, Salapakshi Jai Kumar, Ganga Kaveri Nimmakayala
🤖 gxceed AI 要約
日本語
本論文は、中国の自動車・エネルギー分野を対象に、RFIDによる排出データ収集、IBCによるクロスチェーン相互運用、DP-FLによる不正検知・価格予測を統合したプライバシー保護型ブロックチェーンIoTフレームワークを提案。実験では、インター検証遅延を74.8%削減、不正検出率を93.5～96.8%に向上、取引コストを52.4%削減、市場流動性を35.6%改善した。
English
This paper proposes a privacy-preserving cross-chain federated blockchain-IoT framework for carbon credit management, integrating RFID-based emission data collection, IBC interoperability, and DP-FL for fraud detection and price prediction. Experiments show 74.8% reduction in inter-chain verification latency, 93.5-96.8% fraud detection rate, 52.4% lower transaction overhead, and 35.6% improved market liquidity.
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
日本でもJ-クレジット制度やGXリーグによるカーボンクレジット市場が拡大中。本フレームワークは、異業種間の相互運用性やプライバシー保護、不正防止機能を提供し、日本のカーボンクレジットインフラ設計に示唆を与える。
In the global GX context
Global carbon markets face transparency and scalability challenges. This framework offers a blueprint for secure, interoperable, and intelligent carbon credit systems, relevant for emerging markets and the scaling of Article 6 mechanisms.
👥 読者別の含意
🔬研究者:Novel integration of blockchain, IoT, differential privacy federated learning for carbon credit management, with strong performance metrics.
🏢実務担当者:Technical architecture for building transparent and efficient carbon credit platforms, applicable to cross-sector carbon trading systems.
🏛政策担当者:Insights for designing national carbon market infrastructure that ensures data sovereignty, auditability, and fraud resistance.
📄 Abstract（原文）
The rapid growth of the carbon-credit markets in China has revealed serious flaws in the transparency, security and interoperability across sectors, especially in the automotive PCFN regulatory, ecosystem. The currently deployed blockchain-IoT systems enhance traceability but do not have privacy-protective insight, scalable verification, and interledger dialogue needed to facilitate multi-sector carbon governance. The paper will offer a Privacy-Preserving Cross-Chain Federated Blockchain-IoT Framework, which will combine RFID-enabled real-time capture of emissions, cross-chain interoperability via IBC (Inter-Blockchain Communication) and a differential-privacy-enhanced federated learning (DP-FL) engine to detect fraud and make dynamic predictions of carbon-credit prices. The system uses the hybrid DPoS-sidechain architecture to enable it to achieve high-throughput validation and ensure high-level data sovereignty assurances across the heterogeneous enterprises. The experimental analysis based on simulated national automotive and energy-sector datasets indicates that the suggested framework can help lower inter-chain verification latency by 74.8 percent, increase the cross-sector audit transparency by 41.3 percent, and increase the rate of fraud/anomaly detection by 93.5 percent to 96.8 percent in the case of integrating DP-FL. Moreover, advanced transactions through smart contracts are cross-chain automated settlement which results into overhead of transaction decreased by 52.4 percent and market liquidity enhanced by 35.6 percent in multi-sector simulations. The findings affirm that the suggested system would bolster security, scalability, and intelligence in the carbon-credit ecosystem of China to a considerable extent, providing a scalable base to support countrywide carbon-neutrality goals.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/icoeca68095.2026.11485080first seen 2026-06-23 06:10:45
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