{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101616"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101616","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Using ethereum transactions to deanonymize senders","abstract":"The historic rise of blockchain-based cryptocurrencies to over $327 billion in market capitalization has sparked significant research efforts studying their reliability, performance, and security. Bitcoin, the highest valued cryptocurrency, has received the most thorough scrutiny, with many studies analyzing its peer properties and network health. In contrast, the network layer for Ethereum, the second-largest cryptocurrency, has gone mostly ignored, even though it employs different algorithms for transaction propagation. In this thesis, we perform timing analysis on transactions propagated through Ethereum networks to identify the origin nodes. We build a tool called TxSniper to verify our approach on Ethereum's main network. We find that we can identify the origin with a 70% probability; this method is not always effective due to presence of nodes running clients that use different implementations of transaction propagation.","abstract_html":"The historic rise of blockchain-based cryptocurrencies to over $327 billion in market capitalization has sparked significant research efforts studying their reliability, performance, and security. Bitcoin, the highest valued cryptocurrency, has received the most thorough scrutiny, with many studies analyzing its peer properties and network health. In contrast, the network layer for Ethereum, the second-largest cryptocurrency, has gone mostly ignored, even though it employs different algorithms for transaction propagation. In this thesis, we perform timing analysis on transactions propagated through Ethereum networks to identify the origin nodes. We build a tool called TxSniper to verify our approach on Ethereum's main network. We find that we can identify the origin with a 70% probability; this method is not always effective due to presence of nodes running clients that use different implementations of transaction propagation.","abstract_has_math":false,"creators":["Murali, Siddharth"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Bailey, Michael D"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:17:58Z","date_published":"2018-09-27T16:17:58Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Ethereum","Cryptocurrency","Security","Deanonymization"],"languages":["en"],"rights":["Copyright 2018 Siddharth Murali"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101616","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bailey, Michael D"]},{"key":"dc:creator","label":"Author","values":["Murali, Siddharth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:17:58Z","2018-07-19","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ethereum","Cryptocurrency","Security","Deanonymization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Siddharth Murali"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101616"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The historic rise of blockchain-based cryptocurrencies to over $327 billion in market capitalization has sparked significant research efforts studying their reliability, performance, and security. Bitcoin, the highest valued cryptocurrency, has received the most thorough scrutiny, with many studies analyzing its peer properties and network health. In contrast, the network layer for Ethereum, the second-largest cryptocurrency, has gone mostly ignored, even though it employs different algorithms for transaction propagation. In this thesis, we perform timing analysis on transactions propagated through Ethereum networks to identify the origin nodes. We build a tool called TxSniper to verify our approach on Ethereum's main network. We find that we can identify the origin with a 70% probability; this method is not always effective due to presence of nodes running clients that use different implementations of transaction propagation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Siddharth Murali, accepted the attached license on 2018-07-19 at 10:46.","The student, Siddharth Murali, submitted this Thesis for approval on 2018-07-19 at 10:58.","This Thesis was approved for publication on 2018-07-19 at 11:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12923 on 2018-09-27 at 10:49:18","Made available in DSpace on 2018-09-27T16:17:58Z (GMT). No. of bitstreams: 2 MURALI-THESIS-2018.pdf: 742209 bytes, checksum: e5bfbee3ad636d8ee17225a9c8aa4d9d (MD5) LICENSE.txt: 4213 bytes, checksum: 59179dcb4f1a211bfba46bfa87a5592f (MD5) Previous issue date: 2018-07-19"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using ethereum transactions to deanonymize senders"]}]}],"canonical_facts":{"dc:contributor":["Bailey, Michael D"],"dc:creator":["Murali, Siddharth"],"dc:date":["2018-09-27T16:17:58Z","2018-07-19","2018-08"],"dc:description":["The historic rise of blockchain-based cryptocurrencies to over $327 billion in market capitalization has sparked significant research efforts studying their reliability, performance, and security. Bitcoin, the highest valued cryptocurrency, has received the most thorough scrutiny, with many studies analyzing its peer properties and network health. In contrast, the network layer for Ethereum, the second-largest cryptocurrency, has gone mostly ignored, even though it employs different algorithms for transaction propagation. In this thesis, we perform timing analysis on transactions propagated through Ethereum networks to identify the origin nodes. We build a tool called TxSniper to verify our approach on Ethereum's main network. We find that we can identify the origin with a 70% probability; this method is not always effective due to presence of nodes running clients that use different implementations of transaction propagation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Siddharth Murali, accepted the attached license on 2018-07-19 at 10:46.","The student, Siddharth Murali, submitted this Thesis for approval on 2018-07-19 at 10:58.","This Thesis was approved for publication on 2018-07-19 at 11:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12923 on 2018-09-27 at 10:49:18","Made available in DSpace on 2018-09-27T16:17:58Z (GMT). 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