{"id":"https://openalex.org/W3039812354","doi":"https://doi.org/10.1109/tdsc.2020.3006287","title":"How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning","display_name":"How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3039812354","doi":"https://doi.org/10.1109/tdsc.2020.3006287","mag":"3039812354"},"language":"en","primary_location":{"id":"doi:10.1109/tdsc.2020.3006287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tdsc.2020.3006287","pdf_url":null,"source":{"id":"https://openalex.org/S133795288","display_name":"IEEE Transactions on Dependable and Secure Computing","issn_l":"1545-5971","issn":["1545-5971","1941-0018","2160-9209"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Dependable and Secure Computing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.09370","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lingjuan Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Lingjuan Lyu","raw_affiliation_strings":["Department of Computer Science, National University of Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yitong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yitong Li","raw_affiliation_strings":["School of Computing and Information Systems, The University of Melbourne, Parkville, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, The University of Melbourne, Parkville, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Karthik Nandakumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karthik Nandakumar","raw_affiliation_strings":["IBM Singapore Lab, Singapore"],"affiliations":[{"raw_affiliation_string":"IBM Singapore Lab, Singapore","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiangshan Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiangshan Yu","raw_affiliation_strings":["Faculty of Information Technology, Monash University, Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xingjun Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xingjun Ma","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":2.4684,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.91218306,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.7483999729156494,"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":0.7483999729156494,"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":0.09520000219345093,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.039000000804662704,"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/credibility","display_name":"Credibility","score":0.871399998664856},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7699999809265137},{"id":"https://openalex.org/keywords/reputation","display_name":"Reputation","score":0.6528000235557556},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6256999969482422},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5889999866485596},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.5734999775886536},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.40310001373291016},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.38989999890327454}],"concepts":[{"id":"https://openalex.org/C2780224610","wikidata":"https://www.wikidata.org/wiki/Q1530061","display_name":"Credibility","level":2,"score":0.871399998664856},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7699999809265137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7666000127792358},{"id":"https://openalex.org/C48798503","wikidata":"https://www.wikidata.org/wiki/Q877546","display_name":"Reputation","level":2,"score":0.6528000235557556},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6256999969482422},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5889999866485596},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.5734999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5105000138282776},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.40310001373291016},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3862000107765198},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35440000891685486},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C117387248","wikidata":"https://www.wikidata.org/wiki/Q11186","display_name":"Private network","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.29170000553131104},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.27149999141693115}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tdsc.2020.3006287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tdsc.2020.3006287","pdf_url":null,"source":{"id":"https://openalex.org/S133795288","display_name":"IEEE Transactions on Dependable and Secure Computing","issn_l":"1545-5971","issn":["1545-5971","1941-0018","2160-9209"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Dependable and Secure Computing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2007.09370","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09370","pdf_url":"https://arxiv.org/pdf/2007.09370","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.09370","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.09370","pdf_url":"https://arxiv.org/pdf/2007.09370","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1524581953","https://openalex.org/W2053637704","https://openalex.org/W2120989936","https://openalex.org/W2167372639","https://openalex.org/W2225981128","https://openalex.org/W2435473771","https://openalex.org/W2473418344","https://openalex.org/W2509467699","https://openalex.org/W2591882872","https://openalex.org/W2701059868","https://openalex.org/W2767079719","https://openalex.org/W2781091734","https://openalex.org/W2911915417","https://openalex.org/W2915186774","https://openalex.org/W2950103651","https://openalex.org/W2951832089","https://openalex.org/W2962741900","https://openalex.org/W2972385385","https://openalex.org/W2972882814","https://openalex.org/W3109695251","https://openalex.org/W4205228770","https://openalex.org/W4211068006","https://openalex.org/W4285410122","https://openalex.org/W6638658418","https://openalex.org/W6703420464","https://openalex.org/W6728757088","https://openalex.org/W6735632633","https://openalex.org/W6741832134","https://openalex.org/W6747747879","https://openalex.org/W6747855403","https://openalex.org/W6748503580","https://openalex.org/W6748536023","https://openalex.org/W6756186936","https://openalex.org/W6756847103"],"related_works":[],"abstract_inverted_index":{"This":[0],"article":[1],"first":[2],"considers":[3],"the":[4,76,94,105,132,159,169],"research":[5],"problem":[6],"of":[7,97,125,136],"fairness":[8],"in":[9,31,63],"collaborative":[10,116],"deep":[11,49,118],"learning,":[12,119],"while":[13],"ensuring":[14],"privacy.":[15,38],"A":[16],"novel":[17],"reputation":[18],"system":[19],"is":[20,112],"proposed":[21],"through":[22],"digital":[23],"tokens":[24,124],"and":[25,45,66,100,120,123,134,161,164],"local":[26,61,95,121],"credibility":[27,96,122],"to":[28,36,57,91,114,131,158],"ensure":[29],"fairness,":[30,154],"combination":[32],"with":[33],"differential":[34],"privacy":[35],"guarantee":[37],"In":[39],"particular,":[40],"we":[41],"build":[42],"a":[43,64],"fair":[44,65],"differentially":[46],"private":[47,67],"decentralised":[48],"learning":[50],"framework":[51],"called":[52],"FDPDDL,":[53],"which":[54],"enables":[55],"parties":[56],"derive":[58],"more":[59],"accurate":[60],"models":[62],"manner":[68],"by":[69,82],"using":[70],"our":[71],"developed":[72],"two-stage":[73],"scheme:":[74],"during":[75,104],"initialisation":[77],"stage,":[78,107],"artificial":[79],"samples":[80],"generated":[81],"Differentially":[83,108],"Private":[84,109],"Generative":[85],"Adversarial":[86],"Network":[87],"(DPGAN)":[88],"are":[89,128],"used":[90,113],"mutually":[92],"benchmark":[93,143],"each":[98,126],"party":[99,127],"generate":[101],"initial":[102],"tokens;":[103],"update":[106],"SGD":[110],"(DPSGD)":[111],"facilitate":[115],"privacy-preserving":[117],"updated":[129],"according":[130],"quality":[133],"quantity":[135],"individually":[137],"released":[138],"gradients.":[139],"Experimental":[140],"results":[141],"on":[142],"datasets":[144],"under":[145],"three":[146],"realistic":[147],"settings":[148],"demonstrate":[149],"that":[150],"FDPDDL":[151],"achieves":[152],"high":[153],"yields":[155],"comparable":[156],"accuracy":[157,167],"centralised":[160],"distributed":[162],"frameworks,":[163],"delivers":[165],"better":[166],"than":[168],"standalone":[170],"framework.":[171]},"counts_by_year":[{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-07-10T00:00:00"}
