{"id":"https://openalex.org/W4416241090","doi":"https://doi.org/10.1109/iccv51701.2025.00121","title":"Membership Inference Attacks With False Discovery Rate Control","display_name":"Membership Inference Attacks With False Discovery Rate Control","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416241090","doi":"https://doi.org/10.1109/iccv51701.2025.00121"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.07066","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101768581","display_name":"Chenxu Zhao","orcid":"https://orcid.org/0009-0009-6026-3455"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenxu Zhao","raw_affiliation_strings":["Iowa State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University,Department of Computer Science","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374757","display_name":"Wei Qian","orcid":"https://orcid.org/0000-0002-9563-721X"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Iowa State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University,Department of Computer Science","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062344326","display_name":"Aobo Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aobo Chen","raw_affiliation_strings":["Iowa State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University,Department of Computer Science","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016035883","display_name":"Mengdi Huai","orcid":"https://orcid.org/0000-0001-6368-5973"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengdi Huai","raw_affiliation_strings":["Iowa State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University,Department of Computer Science","institution_ids":["https://openalex.org/I173911158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I173911158"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1216","last_page":"1227"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9277999997138977,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9277999997138977,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.029200000688433647,"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/T11719","display_name":"Data Quality and Management","score":0.006599999964237213,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/false-discovery-rate","display_name":"False discovery rate","score":0.8050000071525574},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.670199990272522},{"id":"https://openalex.org/keywords/false-positive-rate","display_name":"False positive rate","score":0.45739999413490295},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.37310001254081726},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.3327000141143799},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.32409998774528503}],"concepts":[{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.8050000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7229999899864197},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.670199990272522},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5026000142097473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5016000270843506},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48649999499320984},{"id":"https://openalex.org/C95922358","wikidata":"https://www.wikidata.org/wiki/Q5432725","display_name":"False positive rate","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00121","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.07066","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.07066","pdf_url":"https://arxiv.org/pdf/2508.07066","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},{"id":"doi:10.48550/arxiv.2508.07066","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.07066","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.07066","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.07066","pdf_url":"https://arxiv.org/pdf/2508.07066","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"studies":[1],"have":[2,41],"shown":[3],"that":[4,136,162],"deep":[5],"learning":[6,201],"models":[7],"are":[8,55,203],"vulnerable":[9],"to":[10,17,25,68,85,206],"membership":[11,119],"inference":[12,120],"attacks":[13],"(MIAs),":[14],"which":[15,66,123],"aim":[16],"infer":[18],"whether":[19],"a":[20,27,117,160,172],"data":[21,150],"record":[22],"was":[23],"used":[24],"train":[26],"target":[28],"model":[29],"or":[30],"not.":[31],"To":[32,107],"analyze":[33],"and":[34,47,99,198],"study":[35],"these":[36],"vulnerabilities,":[37],"various":[38,192],"MIA":[39,169],"methods":[40,170],"been":[42],"proposed.":[43],"Despite":[44],"the":[45,61,69,76,87,93,100,109,126,129,142,178,183,195,199,208],"significance":[46],"popularity":[48],"of":[49,72,211],"MIAs,":[50],"existing":[51,168],"works":[52],"on":[53,60,128,146],"MIAs":[54],"limited":[56],"in":[57,112,171,191],"providing":[58,177],"guarantees":[59,127],"false":[62,73,88,130],"discovery":[63,89,131],"rate":[64,90],"(FDR),":[65],"refers":[67],"expected":[70],"proportion":[71],"discoveries":[74],"among":[75],"identified":[77],"positive":[78],"discoveries.":[79],"However,":[80],"it":[81],"is":[82,96],"very":[83],"challenging":[84],"ensure":[86],"guarantees,":[91],"because":[92],"underlying":[94],"distribution":[95],"usually":[97],"unknown,":[98],"estimated":[101],"non-member":[102,149],"probabilities":[103],"often":[104],"exhibit":[105],"interdependence.":[106],"tackle":[108],"above":[110],"challenges,":[111],"this":[113],"paper,":[114],"we":[115,134],"design":[116],"novel":[118],"attack":[121],"method,":[122],"can":[124,139,157,163],"provide":[125,141],"rate.":[132],"Additionally,":[133],"show":[135],"our":[137,155,187,212],"method":[138,156],"also":[140,176,204],"marginal":[143],"probability":[144],"guarantee":[145],"labeling":[147],"true":[148],"as":[151,159],"member":[152],"data.":[153],"Notably,":[154],"work":[158],"wrapper":[161],"be":[164],"seamlessly":[165],"integrated":[166],"with":[167],"post-hoc":[173],"manner,":[174],"while":[175],"FDR":[179],"control.":[180],"We":[181],"perform":[182],"theoretical":[184],"analysis":[185],"for":[186],"method.":[188,213],"Extensive":[189],"experiments":[190],"settings":[193],"(e.g.,":[194],"black-box":[196],"setting":[197],"lifelong":[200],"setting)":[202],"conducted":[205],"verify":[207],"desirable":[209],"performance":[210]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
