{"id":"https://openalex.org/W4225004701","doi":"https://doi.org/10.24963/ijcai.2022/532","title":"Membership Inference via Backdooring","display_name":"Membership Inference via Backdooring","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4225004701","doi":"https://doi.org/10.24963/ijcai.2022/532"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/532","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/532","pdf_url":"https://www.ijcai.org/proceedings/2022/0532.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0532.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064530863","display_name":"Hongsheng Hu","orcid":"https://orcid.org/0000-0003-4455-4227"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Hongsheng Hu","raw_affiliation_strings":["University of Auckland"],"affiliations":[{"raw_affiliation_string":"University of Auckland","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088166627","display_name":"Zoran Sal\u010di\u0107","orcid":"https://orcid.org/0000-0001-7714-9848"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Zoran Sal\u010di\u0107","raw_affiliation_strings":["University of Auckland"],"affiliations":[{"raw_affiliation_string":"University of Auckland","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016115576","display_name":"Gillian Dobbie","orcid":"https://orcid.org/0000-0001-7245-0367"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Gillian Dobbie","raw_affiliation_strings":["University of Auckland"],"affiliations":[{"raw_affiliation_string":"University of Auckland","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101808353","display_name":"Jinjun Chen","orcid":"https://orcid.org/0000-0003-1677-9525"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jinjun Chen","raw_affiliation_strings":["Swinburne University of Technology"],"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology","institution_ids":["https://openalex.org/I57093077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015105117","display_name":"Lichao Sun","orcid":"https://orcid.org/0000-0003-1539-7939"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lichao Sun","raw_affiliation_strings":["Lehigh University"],"affiliations":[{"raw_affiliation_string":"Lehigh University","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076120553","display_name":"Xuyun Zhang","orcid":"https://orcid.org/0000-0001-7353-4159"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuyun Zhang","raw_affiliation_strings":["Macquarie University"],"affiliations":[{"raw_affiliation_string":"Macquarie University","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064530863"],"corresponding_institution_ids":["https://openalex.org/I154130895"],"apc_list":null,"apc_paid":null,"fwci":3.3394,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.93651298,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3832","last_page":"3838"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9977999925613403,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8224716782569885},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7676272392272949},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7187609672546387},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6578220129013062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6503638029098511},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4985542297363281},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43040475249290466},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4195984899997711},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3909953236579895},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.21140840649604797}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8224716782569885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7676272392272949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7187609672546387},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6578220129013062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6503638029098511},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4985542297363281},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43040475249290466},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4195984899997711},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3909953236579895},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.21140840649604797},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2022/532","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/532","pdf_url":"https://www.ijcai.org/proceedings/2022/0532.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/532","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/532","pdf_url":"https://www.ijcai.org/proceedings/2022/0532.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3984170559","display_name":null,"funder_award_id":"DE210101458","funder_id":"https://openalex.org/F4320315885","funder_display_name":"Australian Government"}],"funders":[{"id":"https://openalex.org/F4320315885","display_name":"Australian Government","ror":"https://ror.org/0314h5y94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4225004701.pdf","grobid_xml":"https://content.openalex.org/works/W4225004701.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2004302204","https://openalex.org/W2194775991","https://openalex.org/W2535690855","https://openalex.org/W2752128027","https://openalex.org/W2774423163","https://openalex.org/W2795435272","https://openalex.org/W2896457183","https://openalex.org/W2914805061","https://openalex.org/W2934843808","https://openalex.org/W2942091739","https://openalex.org/W2946930197","https://openalex.org/W2963378725","https://openalex.org/W2996800219","https://openalex.org/W3003926725","https://openalex.org/W3013068160","https://openalex.org/W3035556513","https://openalex.org/W3036909764","https://openalex.org/W3042368254","https://openalex.org/W3107337211","https://openalex.org/W3118608800","https://openalex.org/W3155825722","https://openalex.org/W3159603021","https://openalex.org/W3199405963","https://openalex.org/W4287270166","https://openalex.org/W4294506858"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W2140798747","https://openalex.org/W2948169060","https://openalex.org/W2730112582","https://openalex.org/W2110696645","https://openalex.org/W2358580169","https://openalex.org/W2111347279","https://openalex.org/W4399426197","https://openalex.org/W2487211728","https://openalex.org/W2378096925"],"abstract_inverted_index":{"Recently":[0],"issued":[1],"data":[2,33,39,59,67,92,201,206],"privacy":[3,133],"regulations":[4],"like":[5],"GDPR":[6],"(General":[7],"Data":[8],"Protection":[9],"Regulation)":[10],"grant":[11],"individuals":[12],"the":[13,19,38,122,160,165,178,222,244,247,257],"right":[14],"to":[15,28,49,61,75,88,96,103,108,120,126,163,221],"be":[16,104],"forgotten.":[17],"In":[18,148],"context":[20],"of":[21,114,171,212,249,256],"machine":[22,42,50,78],"learning,":[23],"this":[24,109,149],"requires":[25,205],"a":[26,31,55,58,77,84,91,98,105,153,182,189,200,209],"model":[27,184,191],"forget":[29],"about":[30],"training":[32,258],"sample":[34,93],"if":[35],"requested":[36],"by":[37,71,159,199],"owner":[40,60],"(i.e.,":[41],"unlearning).":[43],"As":[44],"an":[45,72],"essential":[46],"step":[47],"prior":[48],"unlearning,":[51],"it":[52],"is":[53,83,260],"still":[54],"challenge":[56,123],"for":[57,130,214,228,263],"tell":[62],"whether":[63,90],"or":[64],"not":[65],"her":[66],"have":[68],"been":[69],"used":[70,95],"unauthorized":[73],"party":[74],"train":[76,97],"learning":[79],"model.":[80],"Membership":[81,172],"inference":[82,117,143,156,216,229],"recently":[85],"emerging":[86],"technique":[87],"identify":[89],"was":[94],"target":[99,223],"model,":[100,224],"and":[101,134,217,238,243],"seems":[102],"promising":[106],"solution":[107],"challenge.":[110,167],"However,":[111],"straightforward":[112],"adoption":[113],"existing":[115],"membership":[116,132,155,215,265],"approaches":[118],"fails":[119],"address":[121,164],"effectively":[124],"due":[125],"being":[127],"originally":[128],"designed":[129],"attacking":[131],"suffering":[135],"from":[136,188],"several":[137],"severe":[138],"limitations":[139],"such":[140],"as":[141],"low":[142],"accuracy":[144],"on":[145,194,235],"well-generalized":[146],"models.":[147],"paper,":[150],"we":[151],"propose":[152],"novel":[154],"approach":[157,170],"inspired":[158],"backdoor":[161],"technology":[162],"said":[166],"Specifically,":[168],"our":[169,250],"Inference":[173],"via":[174],"Backdooring":[175],"(MIB)":[176],"leverages":[177],"key":[179],"observation":[180],"that":[181],"backdoored":[183],"behaves":[185],"very":[186],"differently":[187],"clean":[190],"when":[192],"predicting":[193],"deliberately":[195],"marked":[196],"samples":[197,213],"created":[198],"owner.":[202],"Appealingly,":[203],"MIB":[204],"owners'":[207],"marking":[208,253],"small":[210],"number":[211],"only":[218,254],"black-box":[219],"access":[220],"with":[225],"theoretical":[226],"guarantees":[227],"results.":[230],"We":[231],"perform":[232],"extensive":[233],"experiments":[234],"various":[236],"datasets":[237],"deep":[239],"neural":[240],"network":[241],"architectures,":[242],"results":[245],"validate":[246],"efficacy":[248],"approach,":[251],"e.g.,":[252],"0.1%":[255],"dataset":[259],"practically":[261],"sufficient":[262],"effective":[264],"inference.":[266]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}