  Security incidents such as scams and hacks, have become a major threat to the
health of the blockchain ecosystem, causing billions of dollars in losses each
year for blockchain users. To reveal the real-world entities behind the
pseudonymous blockchain account and recover the stolen funds from the massive
transaction data, much effort has been devoted to tracing the flow of illicit
funds in blockchains recently. However, most current tracing approaches based
on heuristics and taint analysis have limitations in terms of universality,
effectiveness, and efficiency. This paper models the blockchain transaction
records as a blockchain transaction graph and tackles blockchain transaction
tracing as a graph searching task. We propose TRacer, a scalable transaction
tracing tool for account-based blockchains. To infer the relevance between
accounts during graph searching, we develop a novel personalized PageRank
method in TRacer based on the directed, weighted, temporal, and
multi-relationship blockchain transaction graphs. To the best of our knowledge,
TRacer is the first intelligent transaction tracing tool in account-based
blockchains that can handle complex transaction actions in decentralized
finance (DeFi). Experimental results and theoretical analysis prove that TRacer
can complete the transaction tracing task effectively at a low cost. All codes
of TRacer are available at GitHub.
