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

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