[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82000-en":3,"doc-seo-82000-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82000,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","How Reliable Is the Multi-Input Heuristic for Bitcoin Address Clustering in Law Enforcement Contexts","Address clustering is a core blockchain forensics technique used by law enforcement to trace illicit crypto-asset flows. The multi-input heuristic (MIH), which groups addresses likely controlled by the same entity, is widely adopted but seldom tested against reliable ground truth. This work builds a reusable evaluation framework with nine established metrics and assesses MIH using address-to-entity mappings from European crypto asset service providers. Reported-address evaluation shows strong dataset-level performance (recall 0.71) but lower cluster-level precision/recall (0.36/0.44) and near-complete entity-level failures for some services, requiring metric- and entity-aware reliability for legal use.","How Reliable Is the Multi-Input Heuristic for Bitcoin Address Clustering in Law Enforcement Contexts?  \nLeopold Müller University of Bayreuth  \nGermany [leopold.mueller@uni-bayreuth.de](leopold.mueller@uni-bayreuth.de)  \nJana Elsner  \nUniversity of Bayreuth Germany  \nThomas Niedermayer  \nIknaio Cryptoasset Analytics Austria  \nBernhard Haslhofer  \nComplexity Science Hub Austria  \nThomas Goger  \nBavarian Central Office for the Prosecution of Cybercrime Germany  \nNiklas Kühl  \nUniversity of Bayreuth Germany  \nChristian Rückert  \nUniversity of Bayreuth Germany  \narXiv :2607 .074 14v2 [ cs .CR] 9 Jul 2026  \nAbstract  \nAddress clustering is an important technique in blockchain forensics, widely employed by law enforcement to trace illicit crypto asset flows. The multi-input heuristic (MIH), which clusters addresses potentially associated with the same entity, is the most widely used. Yet, despite its broad adoption, the MIH has rarely been evaluated against reliable ground truth data. We implement areusable evaluation framework covering nine established metricsand apply it to ground truth address-to-entity mappings obtained directly from European crypto asset service providers under legally mandated reporting obligations. When evaluation is restricted to reported addresses, the MIH appears strong at dataset level: we observe no mergers between reported services and recover sameservice address pairs with recall 0.71. However, this result is driven by one large service and ignores unlabeled addresses absorbed into full clusters. Metrics that assess the full clusters show substantially lower precision and recall (0 .36 and 0 .44), meaning that services are often only partially recovered or embedded in larger clusters. Entity-level results further reveal near-complete failures for some services. When MIH-based clusters are used to support criminal suspicion, preliminary seizure of crypto assets to secure later forfeiture/ confiscation, or as evidence in trial proceedings, prosecutorsand judges must account for the heuristic’s metric-dependent and entity-dependent reliability.  \nKeywords  \ncrypto currency forensics, multi-input heuristic, address clustering evaluation  \n1 Introduction  \nClustering crypto asset addresses associated with the same realworld entity is a well-established deanonymization technique in blockchain forensics. The underlying intuition is straightforward: once a single address is linked to a known entity, this attribution is often implicitly extended to all addresses within the same cluster [26]. Among clustering methods, the multi-input heuristic (MIH),  \nwhich assumes that all input addresses of a transaction are controlled by the same entity, is the most widely adopted. It is routinely employed either as a standalone clustering rule or as a core component within larger heuristic pipelines [1, 15, 23, 27, 29, 31, 32, 37, 41, 42, 45, 49, 51, 53, 59, 63] . However, despite this widespread adoption in both research and practice, the heuristic has not been rigorously validated against recent real-world ground truth data that is independent of heuristic assumptions.  \nIn criminal proceedings around the world, clustering results can, for example, form a criminal suspicion against the owner of the address in question to request address information from cryptocurrency exchanges. They can also be used as the sole basis fora preliminary seizure to secure the subsequent judicial forfeiture (U.S.)/confiscation (Ger.) of the seized crypto assets, or as additional evidence in court to prove the defendant’s guilt. Although these results are usually only used as supplementary evidence and not asthe sole basis for procedural measures, prosecutors and judges still need to know how reliable the underlying clustering method (such as the MIH) is for the following reasons:  \nIn order to proceed further with the legal process (i.e., to be able to take certain investigative measures, file charges, or determine guilt at trial), prosecutor","cbCainyWtb1fk32B","https://ap.wps.com/l/cbCainyWtb1fk32B","pdf",642765,4,1,13,"English","en",105,"# Introduction\n## Motivation and legal use of clustering results\n## Prior work and limitations\n## Need for ground-truth evaluation","[{\"question\":\"What is the multi-input heuristic (MIH) used for in blockchain forensics?\",\"answer\":\"MIH is a widely used clustering rule that groups transaction input addresses under the assumption that they are controlled by the same real-world entity.\"},{\"question\":\"Why is reliable ground truth important for evaluating MIH?\",\"answer\":\"Because MIH is often used in real legal settings, evaluations based on outdated or simulated data may not reflect current blockchain behavior, so metric values can be misleading.\"},{\"question\":\"What do the evaluation results imply for legal proceedings using MIH clusters?\",\"answer\":\"Performance depends on the evaluation scope and metric: while reported-address results look strong, cluster-level and entity-level failures occur, so prosecutors and judges must account for metric- and entity-dependent reliability when using MIH-derived suspicions or evidence.\"}]",1784177492,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"how-reliable-is-the-multi-input-heuristic-for-bitcoin-address-clustering-in-law-enforcement-contexts","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/how-reliable-is-the-multi-input-heuristic-for-bitcoin-address-clustering-in-law-enforcement-contexts/82000/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the multi-input heuristic (MIH) used for in blockchain forensics?","Question",{"text":75,"@type":76},"MIH is a widely used clustering rule that groups transaction input addresses under the assumption that they are controlled by the same real-world entity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is reliable ground truth important for evaluating MIH?",{"text":80,"@type":76},"Because MIH is often used in real legal settings, evaluations based on outdated or simulated data may not reflect current blockchain behavior, so metric values can be misleading.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the evaluation results imply for legal proceedings using MIH clusters?",{"text":84,"@type":76},"Performance depends on the evaluation scope and metric: while reported-address results look strong, cluster-level and entity-level failures occur, so prosecutors and judges must account for metric- and entity-dependent reliability when using MIH-derived suspicions or 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