Money　laundering　using　cryptocurrency　poses　significant　threats　to　the　blockchain　ecosystem.　Due　to　the　decentralized　and　anonymous　nature　of　cryptocurrencies,　detecting　such　laundering　activities　is　difficult.　Although　substantial　research　has　been　conducted,　almost　all　existing　methods　detect　cryptocurrency　laundering　from　an　individual　perspective,　ignoring　the　fact　that　money　laundering　is　typically　a　group　behavior.　Group　information　should　be　very　helpful　in　laundering　behavior　analysis,　but　such　laundering　groups　are　hard　to　be　recognized　due　to　anonymity　and　diversity　of　purposes　of　cryptocurrency　transactions.　To　address　this　challenge,　we　design　a　multi-persona　grouping　algorithm　that　can　effectively　group　accounts　into　persona　subgraphs.　Then,　we　extract　two　subgraph　features:　cycle　basis　number　and　cycle　overlapping　ratio,　and　build　an　unsupervised　model　to　evaluate　laundering　scores　of　each　subgraph.　Extensive　experiments　on　both　synthetic　and　real-world　datasets　demonstrate　that,　compared　with　existing　methods,　our　proposed　method　can　improve　detection　accuracy　by　17.4　percentage　points　on　average.　To　the　best　of　our　knowledge,　this　is　the　first　work　on　group-based　detection　of　cryptocurrency　laundering.　©　2005-2012　IEEE.