{"id":"https://openalex.org/W4385767643","doi":"https://doi.org/10.24963/ijcai.2023/56","title":"SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles","display_name":"SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385767643","doi":"https://doi.org/10.24963/ijcai.2023/56"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/56","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/56","pdf_url":"https://www.ijcai.org/proceedings/2023/0056.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0056.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001873550","display_name":"Cuong Dinh Tran","orcid":"https://orcid.org/0000-0002-5268-0779"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cuong Tran","raw_affiliation_strings":["Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707314","display_name":"K\u00e8y\u00f9 Zh\u00fc","orcid":"https://orcid.org/0000-0003-3432-5076"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keyu Zhu","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052534316","display_name":"Ferdinando Fioretto","orcid":"https://orcid.org/0000-0002-1381-6776"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ferdinando Fioretto","raw_affiliation_strings":["University of Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035808622","display_name":"Pascal Van Hentenryck","orcid":"https://orcid.org/0000-0001-7085-9994"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pascal Van Hentenryck","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"501","last_page":"509"},"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":0.9851999878883362,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/scalability","display_name":"Scalability","score":0.8199878334999084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7671639919281006},{"id":"https://openalex.org/keywords/private-information-retrieval","display_name":"Private information retrieval","score":0.6043594479560852},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5493199825286865},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5336794257164001},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5039214491844177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4960664212703705},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4839104115962982},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45933476090431213},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.43830522894859314},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42077529430389404},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.413127064704895},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.32145604491233826},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.289961576461792},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.28632688522338867},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1643027663230896}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8199878334999084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7671639919281006},{"id":"https://openalex.org/C99221444","wikidata":"https://www.wikidata.org/wiki/Q1532069","display_name":"Private information retrieval","level":2,"score":0.6043594479560852},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5493199825286865},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5336794257164001},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5039214491844177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4960664212703705},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4839104115962982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45933476090431213},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.43830522894859314},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42077529430389404},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.413127064704895},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32145604491233826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.289961576461792},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.28632688522338867},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1643027663230896},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/56","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/56","pdf_url":"https://www.ijcai.org/proceedings/2023/0056.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/56","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/56","pdf_url":"https://www.ijcai.org/proceedings/2023/0056.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3894295781","display_name":"Collaborative Research: SaTC: CORE: Small: Privacy and Fairness in Critical Decision Making","funder_award_id":"2133169","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6190621094","display_name":"Collaborative Research: RI: Small: End-to-end Learning of Fair and Explainable Schedules for Court Systems","funder_award_id":"2232054","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385767643.pdf"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1873763122","https://openalex.org/W2473418344","https://openalex.org/W2785361959","https://openalex.org/W2785487418","https://openalex.org/W2946346162","https://openalex.org/W2950943617","https://openalex.org/W3003636647","https://openalex.org/W3007366319","https://openalex.org/W3089046691","https://openalex.org/W3110114991","https://openalex.org/W3112020351","https://openalex.org/W3135401435","https://openalex.org/W3177313640","https://openalex.org/W3189509218","https://openalex.org/W3201507040","https://openalex.org/W3212184486","https://openalex.org/W4221150131","https://openalex.org/W4226553020","https://openalex.org/W4286961857","https://openalex.org/W4287115237","https://openalex.org/W4288346602","https://openalex.org/W4289129244","https://openalex.org/W4297795193","https://openalex.org/W4302016406","https://openalex.org/W4320855591","https://openalex.org/W4385767643"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2596305496","https://openalex.org/W4285322112","https://openalex.org/W4292794239"],"abstract_inverted_index":{"A":[0,72],"critical":[1],"concern":[2],"in":[3,33],"data-driven":[4],"processes":[5],"is":[6,27,79],"to":[7,29,42,68,80,90],"build":[8],"models":[9,89,114,133],"whose":[10],"outcomes":[11],"do":[12],"not":[13,38],"discriminate":[14],"against":[15],"some":[16],"protected":[17],"groups.":[18],"In":[19,119],"learning":[20],"tasks,":[21],"knowledge":[22],"of":[23,60,75,84,111,129],"the":[24,58,76,82,100,109,112],"group":[25],"attributes":[26,36],"essential":[28],"ensure":[30],"non-discrimination,":[31],"but":[32],"practice,":[34],"these":[35],"may":[37],"be":[39],"available":[40],"due":[41],"legal":[43],"and":[44,86,94,105,107,126,131],"ethical":[45],"requirements.":[46],"To":[47],"address":[48],"this":[49,51,121],"challenge,":[50],"paper":[52,98],"studies":[53],"a":[54,92],"model":[55,78],"that":[56],"protects":[57],"privacy":[59],"individuals\u2019":[61],"sensitive":[62],"information":[63],"while":[64],"also":[65],"allowing":[66],"it":[67],"learn":[69],"non-discriminatory":[70],"predictors.":[71],"key":[73],"feature":[74],"proposed":[77,113],"enable":[81],"use":[83],"off-the-shelves":[85],"non-private":[87],"fair":[88,95,132],"create":[91],"privacy-preserving":[93],"model.":[96],"The":[97],"analyzes":[99],"relation":[101],"between":[102],"accuracy,":[103],"privacy,":[104],"fairness,":[106],"assesses":[108],"benefits":[110],"on":[115],"several":[116],"prediction":[117],"tasks.":[118],"particular,":[120],"proposal":[122],"allows":[123],"both":[124],"scalable":[125],"accurate":[127],"training":[128],"private":[130],"for":[134],"very":[135],"large":[136],"neural":[137],"networks.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}