{"id":"https://openalex.org/W4200218970","doi":"https://doi.org/10.1109/wifs53200.2021.9648378","title":"Differentially Private Generative Adversarial Networks with Model Inversion","display_name":"Differentially Private Generative Adversarial Networks with Model Inversion","publication_year":2021,"publication_date":"2021-12-07","ids":{"openalex":"https://openalex.org/W4200218970","doi":"https://doi.org/10.1109/wifs53200.2021.9648378","pmid":"https://pubmed.ncbi.nlm.nih.gov/35517057"},"language":"en","primary_location":{"id":"doi:10.1109/wifs53200.2021.9648378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs53200.2021.9648378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2201.03139","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100752827","display_name":"Dongjie Chen","orcid":"https://orcid.org/0000-0003-1336-7787"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongjie Chen","raw_affiliation_strings":["UC Davis, Davis, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Davis, Davis, CA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022090297","display_name":"Sen-ching S. Cheung","orcid":"https://orcid.org/0000-0002-9207-5514"},"institutions":[{"id":"https://openalex.org/I143302722","display_name":"University of Kentucky","ror":"https://ror.org/02k3smh20","country_code":"US","type":"education","lineage":["https://openalex.org/I143302722"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sen-ching Samson Cheung","raw_affiliation_strings":["University of Kentucky, Lexington, KY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kentucky, Lexington, KY","institution_ids":["https://openalex.org/I143302722"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017383502","display_name":"Chen\u2010Nee Chuah","orcid":"https://orcid.org/0000-0002-2772-387X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen-Nee Chuah","raw_affiliation_strings":["UC Davis, Davis, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Davis, Davis, CA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007281012","display_name":"Sally Ozonoff","orcid":"https://orcid.org/0000-0002-1230-0794"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sally Ozonoff","raw_affiliation_strings":["UC Davis, Davis, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Davis, Davis, CA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1711,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.82472413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"2021","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","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/T11448","display_name":"Face recognition and analysis","score":0.9975000023841858,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9945999979972839,"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/computer-science","display_name":"Computer science","score":0.706647515296936},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.6463243961334229},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6443612575531006},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.625534176826477},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.6167113184928894},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5691289901733398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5027110576629639},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.49209922552108765},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.47128620743751526},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4703044295310974},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.45976606011390686},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42795276641845703},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.42276644706726074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35005006194114685},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3431112766265869},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3182697296142578},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23895540833473206},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08200204372406006}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.706647515296936},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.6463243961334229},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6443612575531006},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.625534176826477},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.6167113184928894},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5691289901733398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5027110576629639},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.49209922552108765},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.47128620743751526},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4703044295310974},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.45976606011390686},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42795276641845703},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.42276644706726074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35005006194114685},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3431112766265869},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3182697296142578},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23895540833473206},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08200204372406006},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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":4,"locations":[{"id":"doi:10.1109/wifs53200.2021.9648378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs53200.2021.9648378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"},{"id":"pmid:35517057","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35517057","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ... IEEE International Workshop on Information Forensics and Security. IEEE International Workshop on Information Forensics and Security","raw_type":null},{"id":"pmh:oai:arXiv.org:2201.03139","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.03139","pdf_url":"https://arxiv.org/pdf/2201.03139","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:pubmedcentral.nih.gov:9070036","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9070036","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Int Workshop Inf Forensics Secur","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2201.03139","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.03139","pdf_url":"https://arxiv.org/pdf/2201.03139","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://openalex.org/G3417464405","display_name":null,"funder_award_id":"R01 MH121344","funder_id":"https://openalex.org/F4320337346","funder_display_name":"National Institute of Mental Health"},{"id":"https://openalex.org/G559585068","display_name":"Novel video-based approaches for detection of autism risk in the first year of life","funder_award_id":"5r01mh121344-05","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337346","display_name":"National Institute of Mental Health","ror":"https://ror.org/04xeg9z08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W607505555","https://openalex.org/W1976748406","https://openalex.org/W2051267297","https://openalex.org/W2335728318","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2618858825","https://openalex.org/W2789226849","https://openalex.org/W2804078698","https://openalex.org/W2908109788","https://openalex.org/W2911915417","https://openalex.org/W2961396908","https://openalex.org/W2963073614","https://openalex.org/W2963420272","https://openalex.org/W2967880504","https://openalex.org/W3104110119","https://openalex.org/W3118608800","https://openalex.org/W4205206005","https://openalex.org/W4205228770","https://openalex.org/W4236362309","https://openalex.org/W4239657488","https://openalex.org/W4288023537","https://openalex.org/W4294583247","https://openalex.org/W4295521014","https://openalex.org/W4320013936","https://openalex.org/W4394670483","https://openalex.org/W6735913928","https://openalex.org/W6738590841","https://openalex.org/W6738960736","https://openalex.org/W6748503580","https://openalex.org/W6752378368","https://openalex.org/W6757641797","https://openalex.org/W6770308229","https://openalex.org/W6779062925","https://openalex.org/W6779669310","https://openalex.org/W6791809807"],"related_works":["https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013","https://openalex.org/W4362706668","https://openalex.org/W3171021120","https://openalex.org/W3008318776","https://openalex.org/W2041416246","https://openalex.org/W3020853991"],"abstract_inverted_index":{"To":[0],"protect":[1],"sensitive":[2],"data":[3,71],"in":[4,24,54],"training":[5,46],"a":[6,80,85,104],"Generative":[7],"Adversarial":[8],"Network":[9],"(GAN),":[10],"the":[11,31,36,45,48,55,69,76,116,131],"standard":[12,95,117],"approach":[13,114],"is":[14,28,72],"to":[15,30,75],"use":[16],"differentially":[17],"private":[18,70],"(DP)":[19],"stochastic":[20],"gradient":[21],"descent":[22],"method":[23,67,119],"which":[25],"controlled":[26],"noise":[27],"added":[29],"gradients.":[32],"The":[33],"quality":[34],"of":[35,47,57],"output":[37],"synthetic":[38],"samples":[39],"can":[40],"be":[41],"adversely":[42],"affected":[43],"and":[44,98,127],"network":[49],"may":[50],"not":[51],"even":[52],"converge":[53],"presence":[56],"these":[58],"noises.":[59],"We":[60],"propose":[61],"Differentially":[62],"Private":[63],"Model":[64],"Inversion":[65],"(DPMI)":[66],"where":[68],"first":[73],"mapped":[74],"latent":[77],"space":[78],"via":[79],"public":[81],"generator,":[82],"followed":[83],"by":[84],"lower-dimensional":[86],"DP-GAN":[87,118],"with":[88],"better":[89],"convergent":[90],"properties.":[91],"Experimental":[92],"results":[93],"on":[94,103,121],"datasets":[96],"CIFAR10":[97],"SVHN":[99],"as":[100,102],"well":[101],"facial":[105],"landmark":[106],"dataset":[107],"for":[108],"Autism":[109],"screening":[110],"show":[111],"that":[112],"our":[113],"outperforms":[115],"based":[120],"Inception":[122,125],"Score,":[123],"Frechet":[124],"Distance,":[126],"classification":[128],"accuracy":[129],"under":[130],"same":[132],"privacy":[133],"guarantee.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}