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As one of the most popular blockchain platforms supporting smart contracts,Ethereum has caught the interest of both investors and criminals. Differentlyfrom traditional financial scenarios, executing Know Your Customer verificationon Ethereum is rather difficult due to the pseudonymous nature of theblockchain. Fortunately, as the transaction records stored in the Ethereumblockchain are publicly accessible, we can understand the behavior of accountsor detect illicit activities via transaction mining. Existing risk controltechniques have primarily been developed from the perspectives ofde-anonymizing address clustering and illicit account classification. However,these techniques cannot be used to ascertain the potential risks for allaccounts and are limited by specific heuristic strategies or insufficient labelinformation. These constraints motivate us to seek an effective rating methodfor quantifying the spread of risk in a transaction network. To the best of ourknowledge, we are the first to address the problem of account risk rating onEthereum by proposing a novel model called RiskProp, which includes ade-anonymous score to measure transaction anonymity and a network propagationmechanism to formulate the relationships between accounts and transactions. Wedemonstrate the effectiveness of RiskProp in overcoming the limitations ofexisting models by conducting experiments on real-world datasets from Ethereum.Through case studies on the detected high-risk accounts, we demonstrate thatthe risk assessment by RiskProp can be used to provide warnings for investorsand protect them from possible financial losses, and the superior performanceof risk score-based account classification experiments further verifies theeffectiveness of our rating method.

RiskProp: Account Risk Rating on Ethereum via De-anonymous Score and Network Propagation