{"id":"https://openalex.org/W3129723090","doi":"https://doi.org/10.1109/trustcom50675.2020.00190","title":"Membership Inference Attacks: Analysis and Mitigation","display_name":"Membership Inference Attacks: Analysis and Mitigation","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3129723090","doi":"https://doi.org/10.1109/trustcom50675.2020.00190","mag":"3129723090"},"language":"en","primary_location":{"id":"doi:10.1109/trustcom50675.2020.00190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/trustcom50675.2020.00190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038225094","display_name":"M S R Shuvo","orcid":null},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Md Shamimur Rahman Shuvo","raw_affiliation_strings":["University of New Brunswick, Canada","Department of Computer Science, University of New Brunswick, Fredericton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Brunswick, Canada","institution_ids":["https://openalex.org/I106938459"]},{"raw_affiliation_string":"Department of Computer Science, University of New Brunswick, Fredericton, Canada","institution_ids":["https://openalex.org/I106938459"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060625032","display_name":"Dima Alhadidi","orcid":"https://orcid.org/0000-0002-2858-5712"},"institutions":[{"id":"https://openalex.org/I74413500","display_name":"University of Windsor","ror":"https://ror.org/01gw3d370","country_code":"CA","type":"education","lineage":["https://openalex.org/I74413500"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dima Alhadidi","raw_affiliation_strings":["University of Windsor, Canada","School of Computer Science, University of Windsor, Windsor, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Windsor, Canada","institution_ids":["https://openalex.org/I74413500"]},{"raw_affiliation_string":"School of Computer Science, University of Windsor, Windsor, Canada","institution_ids":["https://openalex.org/I74413500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4609,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.67282027,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1410","last_page":"1419"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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":0.9997000098228455,"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":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7760133743286133},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7759629487991333},{"id":"https://openalex.org/keywords/shadow","display_name":"Shadow (psychology)","score":0.7125582695007324},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6187838315963745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687587261199951},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.47286152839660645},{"id":"https://openalex.org/keywords/attack-model","display_name":"Attack model","score":0.4659615159034729},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4462602138519287},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41216111183166504},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2015971541404724}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7760133743286133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7759629487991333},{"id":"https://openalex.org/C117797892","wikidata":"https://www.wikidata.org/wiki/Q286363","display_name":"Shadow (psychology)","level":2,"score":0.7125582695007324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6187838315963745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687587261199951},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.47286152839660645},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.4659615159034729},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4462602138519287},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41216111183166504},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2015971541404724},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/trustcom50675.2020.00190","is_oa":false,"landing_page_url":"https://doi.org/10.1109/trustcom50675.2020.00190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1054244022","display_name":"Privacy-Preserving Machine Learning Techniques","funder_award_id":"rgpin-2019-05689","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1964175594","https://openalex.org/W1985511977","https://openalex.org/W1992926795","https://openalex.org/W2000359198","https://openalex.org/W2040228409","https://openalex.org/W2053637704","https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2119874464","https://openalex.org/W2399682466","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2753840555","https://openalex.org/W2778284298","https://openalex.org/W2786233556","https://openalex.org/W2798657499","https://openalex.org/W2799803467","https://openalex.org/W2884943453","https://openalex.org/W2887995258","https://openalex.org/W2912023992","https://openalex.org/W2927692314","https://openalex.org/W2930926105","https://openalex.org/W2932329902","https://openalex.org/W2948030332","https://openalex.org/W2948788074","https://openalex.org/W2950943617","https://openalex.org/W2963378725","https://openalex.org/W2963695762","https://openalex.org/W2971018118","https://openalex.org/W2972280210","https://openalex.org/W2983140679","https://openalex.org/W3000899587","https://openalex.org/W3002731020","https://openalex.org/W3010177353","https://openalex.org/W3102360395","https://openalex.org/W3103245149","https://openalex.org/W4288334474","https://openalex.org/W6637242042","https://openalex.org/W6663928093","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6677855611","https://openalex.org/W6712691055","https://openalex.org/W6743732567","https://openalex.org/W6744389324","https://openalex.org/W6747553010","https://openalex.org/W6750182894","https://openalex.org/W6750799548","https://openalex.org/W6751901350"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W2381570729","https://openalex.org/W1976205134","https://openalex.org/W4248336175","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W3009369890","https://openalex.org/W4312490297","https://openalex.org/W4214858327"],"abstract_inverted_index":{"Given":[0],"a":[1,6,27,105],"machine":[2,39],"learning":[3,40],"model":[4],"and":[5,42,78,97,114,146,155,197],"record,":[7],"membership":[8,84,182],"attacks":[9],"determine":[10],"whether":[11],"this":[12,112,130,135,161],"record":[13],"was":[14],"used":[15,36],"as":[16],"part":[17],"of":[18,66,99,144,149,163,169,190],"the":[19,45,51,64,67,75,80,94,100,125,142,147,170,177,181,188,191,199],"model's":[20,127],"training":[21,76,150,167],"dataset.":[22],"Membership":[23],"inference":[24,183],"can":[25],"present":[26],"risk":[28],"to":[29,37,44,50,107,116,141],"private":[30],"datasets":[31,34,77],"if":[32],"these":[33,86],"are":[35,60,90],"train":[38],"models":[41,47,59,89,145],"access":[43],"resulting":[46],"is":[48,104],"open":[49],"public.":[52],"To":[53],"construct":[54],"attack":[55,113,136,164,184],"models,":[56],"multiple":[57],"shadow":[58,101],"created":[61],"that":[62,121,176],"imitate":[63],"behaviour":[65],"target":[68,126,171,192],"model,":[69],"but":[70],"for":[71],"which":[72],"we":[73,132,195],"know":[74],"thus":[79],"ground":[81],"truth":[82],"about":[83,111],"in":[85],"datasets.":[87],"Attack":[88],"then":[91],"trained":[92],"on":[93],"labeled":[95],"inputs":[96],"outputs":[98],"models.":[102],"There":[103],"desideratum":[106],"conduct":[108],"more":[109],"analysis":[110],"accordingly":[115],"provide":[117],"robust":[118],"mitigation":[119,158,201],"techniques":[120,159,202],"will":[122],"not":[123],"affect":[124],"utility.":[128],"In":[129],"paper,":[131],"empirically":[133],"analyzed":[134],"from":[137],"different":[138,157,166],"perspectives":[139],"related":[140],"number":[143],"type":[148,162],"algorithms.":[151],"We":[152],"also":[153],"proposed":[154],"evaluated":[156],"against":[160],"considering":[165],"algorithms":[168],"model.":[172,193],"Our":[173],"experiments":[174],"show":[175],"defence":[178],"strategies":[179],"mitigate":[180],"considerably":[185],"while":[186],"preserving":[187],"utility":[189],"Finally,":[194],"summarized":[196],"compared":[198],"existing":[200],"with":[203],"our":[204],"results.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
