チェーン分析
チェーン分析は、ブロックチェーン取引を調査して資金の流れを追跡し、エンティティを特定し、違法行為を検出し、オンチェーンデータから洞察を得る手法です。
Chain analysis (or blockchain analysis) encompasses techniques for extracting intelligence from public blockchain data. While addresses are pseudonymous, transaction patterns, clustering algorithms, and external data integration can often link addresses to real-world identities or entity types.
Key techniques include: address clustering (grouping addresses controlled by same entity), transaction graph analysis (following fund flows), heuristics (common spend, change detection), timing analysis, and integration with exchange KYC data. Machine learning increasingly automates pattern detection.
Major chain analysis providers include Chainalysis, Elliptic, and TRM Labs, serving law enforcement, exchanges, and compliance teams. Open-source alternatives and data platforms like Dune, Arkham, and Nansen provide varying levels of analysis capabilities.
Use cases span compliance (AML/KYC verification), investigations (tracking stolen funds, ransomware payments), market intelligence (whale tracking, fund flows), and research (DeFi metrics, network analysis). Privacy coins and mixing services attempt to defeat chain analysis but face increasing regulatory pressure.
graph LR
Center["チェーン分析"]:::main
Pre_blockchain["blockchain"]:::pre --> Center
click Pre_blockchain "/terms/blockchain"
Pre_cryptocurrency["cryptocurrency"]:::pre --> Center
click Pre_cryptocurrency "/terms/cryptocurrency"
Rel_blockchain_forensics["blockchain-forensics"]:::related -.-> Center
click Rel_blockchain_forensics "/terms/blockchain-forensics"
Rel_transaction_tracing["transaction-tracing"]:::related -.-> Center
click Rel_transaction_tracing "/terms/transaction-tracing"
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classDef pre fill:#0f172a,stroke:#3b82f6,color:#94a3b8,rx:5,ry:5;
classDef child fill:#0f172a,stroke:#10b981,color:#94a3b8,rx:5,ry:5;
classDef related fill:#0f172a,stroke:#8b5cf6,stroke-dasharray: 5 5,color:#94a3b8,rx:5,ry:5;
linkStyle default stroke:#4b5563,stroke-width:2px;
🧒 5歳でもわかるように説明
すべてのお金のやり取りが書かれた公開ノートを使って、探偵がお金の流れを追いかけ、誰が送ったかを突き止めるようなものです。
🤓 Expert Deep Dive
グラフ理論とデータマイニングを利用して、同一実体に属するアドレスをクラスタリングし、ランサムウェアなどの不正パターンを認識します。