{"id":"https://openalex.org/W4409150146","doi":"https://doi.org/10.1145/3690624.3709170","title":"Benchmarking Fraud Detectors on Private Graph Data","display_name":"Benchmarking Fraud Detectors on Private Graph Data","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409150146","doi":"https://doi.org/10.1145/3690624.3709170"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709170","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709170","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3690624.3709170","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039479484","display_name":"Alexander Goldberg","orcid":"https://orcid.org/0000-0003-1818-6387"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexander Goldberg","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076026636","display_name":"Giulia Fanti","orcid":"https://orcid.org/0000-0002-7671-2624"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Giulia Fanti","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034272491","display_name":"Nihar B. Shah","orcid":"https://orcid.org/0000-0001-5158-9677"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nihar Shah","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001070941","display_name":"Zhiwei Steven Wu","orcid":"https://orcid.org/0000-0002-8125-8227"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven Wu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039479484"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03182505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"378","last_page":"389"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.8889625072479248},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6007866263389587},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4339020550251007},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4264342784881592},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.29068365693092346},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14532572031021118},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1010696291923523}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8889625072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6007866263389587},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4339020550251007},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4264342784881592},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.29068365693092346},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14532572031021118},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1010696291923523},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3690624.3709170","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709170","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.22347","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.22347","pdf_url":"https://arxiv.org/pdf/2507.22347","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3690624.3709170","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709170","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W1834627138","https://openalex.org/W1979077921","https://openalex.org/W1992461601","https://openalex.org/W2080900058","https://openalex.org/W2101771965","https://openalex.org/W2108409407","https://openalex.org/W2138865266","https://openalex.org/W2160266360","https://openalex.org/W2236522663","https://openalex.org/W2291045511","https://openalex.org/W2431360964","https://openalex.org/W2558896083","https://openalex.org/W2560674852","https://openalex.org/W2969985801","https://openalex.org/W2971594114","https://openalex.org/W2990138404","https://openalex.org/W3022269570","https://openalex.org/W3068123808","https://openalex.org/W3100460887","https://openalex.org/W3102969158","https://openalex.org/W3164964801","https://openalex.org/W3178455632","https://openalex.org/W4205228770","https://openalex.org/W4284892133","https://openalex.org/W4376626901","https://openalex.org/W4402264416","https://openalex.org/W6824583106"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W1490753184","https://openalex.org/W2284465472","https://openalex.org/W2291782699"],"abstract_inverted_index":{"We":[0,30,77,150,165,178],"introduce":[1],"the":[2,32,60,75,97,111,197],"novel":[3],"problem":[4],"of":[5,16,42,102,114,141,148,170],"benchmarking":[6],"fraud":[7,17,43,57],"detectors":[8,44,58],"on":[9,67,83,96,219],"private":[10,69,134],"graph-structured":[11],"data.":[12,177],"Currently,":[13],"many":[14],"types":[15],"are":[18],"managed":[19],"in":[20,125,132],"part":[21],"by":[22,110],"automated":[23],"detection":[24],"algorithms":[25,66,109,156],"that":[26,86,183,196],"operate":[27],"over":[28],"graphs.":[29],"consider":[31],"scenario":[33],"where":[34],"a":[35,68,79,103,133,137,144,159],"data":[36,61,93,129],"holder":[37],"wishes":[38],"to":[39,45,59,90,154,231],"outsource":[40],"development":[41],"third":[46,53],"parties":[47,54],"(e.g.,":[48],"vendors":[49],"or":[50],"researchers).":[51],"The":[52],"submit":[55],"their":[56],"holder,":[62],"who":[63],"evaluates":[64],"these":[65],"dataset":[70],"and":[71,116,173,210],"then":[72,151],"publicly":[73],"communicates":[74],"results.":[76,99],"propose":[78],"realistic":[80],"privacy":[81,162],"attack":[82,120],"this":[84,223],"system":[85],"allows":[87],"an":[88],"adversary":[89],"de-anonymize":[91],"individuals'":[92,128],"based":[94],"only":[95],"evaluation":[98],"In":[100],"simulations":[101],"privacy-sensitive":[104],"benchmark":[105,155],"for":[106],"facial":[107],"recognition":[108],"National":[112],"Institute":[113],"Standards":[115],"Technology":[117],"(NIST),":[118],"our":[119],"achieves":[121],"near":[122],"perfect":[123],"accuracy":[124],"identifying":[126],"whether":[127],"is":[130],"present":[131],"dataset,":[135],"with":[136],"True":[138],"Positive":[139,146],"Rate":[140,147],"0.98":[142],"at":[143],"False":[145],"0.00.":[149],"study":[152],"how":[153],"while":[157],"satisfying":[158],"formal":[160],"differential":[161],"(DP)":[163],"guarantee.":[164],"empirically":[166],"evaluate":[167],"two":[168],"classes":[169],"solutions:":[171],"subsample-and-aggregate":[172],"DP":[174,201],"synthetic":[175],"graph":[176,208],"demonstrate":[179],"through":[180],"extensive":[181],"experiments":[182],"current":[184],"approaches":[185],"do":[186],"not":[187],"provide":[188],"utility":[189],"when":[190],"guaranteeing":[191],"DP.":[192],"Our":[193],"results":[194],"indicate":[195],"error":[198],"arising":[199],"from":[200,206,212],"trades":[202],"off":[203],"between":[204],"bias":[205],"distorting":[207],"structure":[209],"variance":[211],"adding":[213],"random":[214],"noise.":[215],"Current":[216],"methods":[217,229],"lie":[218],"different":[220],"points":[221],"along":[222],"bias-variance":[224],"trade-off,":[225],"but":[226],"more":[227],"complex":[228],"tend":[230],"require":[232],"high-variance":[233],"noise":[234],"addition,":[235],"undermining":[236],"utility.":[237]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
