Chain Analysis
The practice of studying public blockchain data to trace fund flows, identify patterns, and attribute addresses to real-world entities.
Chain analysis is the broader discipline of examining public, permanent blockchain transaction data to understand fund flows, identify behavioral patterns, and attribute addresses or clusters of addresses to real-world entities such as exchanges, services, or individuals. It draws on specific techniques including entity clustering (see Kaavon's "Entity Clustering" glossary definition), taint analysis (see Kaavon's "Taint Analysis" glossary definition), and timing or amount-correlation analysis, combined often with off-chain data like exchange KYC records or publicly known addresses.
Chain analysis is used for a wide range of purposes, including compliance and anti-money-laundering screening by exchanges and regulators, law enforcement investigations tracing stolen or illicit funds, academic and market research into on-chain activity, and general due diligence on counterparties. Because blockchain data is public and permanent, chain analysis capabilities tend to improve over time, meaning transactions considered anonymous or hard to trace at the time they occurred can sometimes become attributable years later as analysis techniques and available data improve.
Examples
- Law enforcement agencies have used chain analysis to trace funds from major exchange hacks across many hops and eventually identify cash-out points at centralized exchanges.
- Because blockchain data is permanent, chain analysis can sometimes retroactively de-anonymize transactions years after they occurred, as clustering and attribution techniques improve.