{"id":"https://openalex.org/W3036808490","doi":null,"title":"Robust Differentially Private Training of Deep Neural Networks","display_name":"Robust Differentially Private Training of Deep Neural Networks","publication_year":2020,"publication_date":"2020-06-19","ids":{"openalex":"https://openalex.org/W3036808490","mag":"3036808490"},"language":"en","primary_location":{"id":"mag:3036808490","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.10919.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.10919.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074437829","display_name":"Ali Davody","orcid":"https://orcid.org/0000-0003-2687-4577","observed_orcids":["https://orcid.org/0000-0003-2687-4577"]},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ali Davody","raw_affiliation_strings":["Saarland Univ,"],"raw_orcid":"https://orcid.org/0000-0003-2687-4577","affiliations":[{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088658365","display_name":"David Ifeoluwa Adelani","orcid":"https://orcid.org/0000-0002-0193-2083","observed_orcids":["https://orcid.org/0000-0002-0193-2083"]},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"David Ifeoluwa Adelani","raw_affiliation_strings":["Saarland Univ,"],"raw_orcid":"https://orcid.org/0000-0002-0193-2083","affiliations":[{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065334335","display_name":"Thomas Kleinbauer","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Kleinbauer","raw_affiliation_strings":["Saarland Univ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008875255","display_name":"Dietrich Klakow","orcid":"https://orcid.org/0000-0002-4147-9690","observed_orcids":["https://orcid.org/0000-0002-4147-9690"]},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dietrich Klakow","raw_affiliation_strings":["Saarland Univ,"],"raw_orcid":"https://orcid.org/0000-0002-4147-9690","affiliations":[{"raw_affiliation_string":"Saarland Univ,","institution_ids":["https://openalex.org/I91712215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91712215"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":9.999999747378752e-05,"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":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"differential privacy","score":0.9760000109672546},{"id":"https://openalex.org/keywords/deep-neural-network-training","display_name":"deep neural network training","score":0.7630000114440918},{"id":"https://openalex.org/keywords/normalization-layers","display_name":"normalization layers","score":0.6850000023841858},{"id":"https://openalex.org/keywords/batch-normalization","display_name":"batch normalization","score":0.7879999876022339},{"id":"https://openalex.org/keywords/layer-normalization","display_name":"layer normalization","score":0.9089999794960022},{"id":"https://openalex.org/keywords/scale-invariance","display_name":"scale invariance","score":0.7379999756813049},{"id":"https://openalex.org/keywords/privacy-utility-trade-off","display_name":"privacy-utility trade-off","score":0.7419999837875366}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.8115800619125366},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.7542442083358765},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7361880540847778},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6539537310600281},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.608184814453125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5711839199066162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5452970862388611},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5095353722572327},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5039460062980652},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49363815784454346},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.4691756069660187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4084290862083435},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3910829424858093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35111677646636963},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3036808490","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.10919.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3036808490","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.10919.pdf","pdf_url":null,"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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1557833142","https://openalex.org/W1646152356","https://openalex.org/W1686810756","https://openalex.org/W1873763122","https://openalex.org/W2027595342","https://openalex.org/W2053637704","https://openalex.org/W2112796928","https://openalex.org/W2127342049","https://openalex.org/W2139072039","https://openalex.org/W2194775991","https://openalex.org/W2473418344","https://openalex.org/W2520442116","https://openalex.org/W2594311007","https://openalex.org/W2606537796","https://openalex.org/W2887654530","https://openalex.org/W2899771611","https://openalex.org/W2902986194","https://openalex.org/W2949117887","https://openalex.org/W2951266961","https://openalex.org/W2964121744","https://openalex.org/W2964881778","https://openalex.org/W2969261410","https://openalex.org/W2970971581","https://openalex.org/W2999251207","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W4225789426","https://openalex.org/W4376626823","https://openalex.org/W3188864953","https://openalex.org/W4416102263","https://openalex.org/W3088349136","https://openalex.org/W3180576191","https://openalex.org/W4323697236","https://openalex.org/W7166180820","https://openalex.org/W3092543885","https://openalex.org/W3101063537"],"abstract_inverted_index":{"Differentially":[0],"private":[1,84],"stochastic":[2,10],"gradient":[3,11],"descent":[4,12],"(DPSGD)":[5],"is":[6,45,148,252],"a":[7,46,75,93,116,149,165,204,208,225],"variation":[8],"of":[9,29,38,81,100,119,128,151,156,211,241],"based":[13,64],"on":[14,65,97,160,207,213,232],"the":[15,27,49,79,82,98,126,133,136,152,233],"Differential":[16],"Privacy":[17],"(DP)":[18],"paradigm":[19],"which":[20,72],"can":[21,122],"mitigate":[22],"privacy":[23,63,177],"threats":[24],"arising":[25],"from":[26,182],"presence":[28],"sensitive":[30],"information":[31],"in":[32,48,78],"training":[33,39,170],"data.":[34],"One":[35],"major":[36],"drawback":[37],"deep":[40,101,171],"neural":[41,102,129,172],"networks":[42,103,130,137,173],"with":[43,104,140,238],"DPSGD":[44,231],"reduction":[47],"model's":[50],"accuracy.":[51],"In":[52,107,221],"this":[53,146,255],"paper,":[54],"we":[55,109,163,223],"propose":[56,164],"an":[57],"alternative":[58],"method":[59,197],"for":[60,169],"preserving":[61],"data":[62],"introducing":[66],"noise":[67,121],"through":[68],"learnable":[69],"probability":[70],"distributions,":[71],"leads":[73],"to":[74,113,125,192],"significant":[76],"improvement":[77,229],"utility":[80],"resulting":[83],"models.":[85],"We":[86,143],"also":[87],"demonstrate":[88],"that":[89,111,145],"normalization":[90,141,157,190],"layers":[91],"have":[92],"large":[94,117],"beneficial":[95],"impact":[96],"performance":[99,194],"noisy":[105],"parameters.":[106],"particular,":[108,222],"show":[110],"contrary":[112],"general":[114],"belief,":[115],"amount":[118],"random":[120],"be":[123],"added":[124],"weights":[127,181],"without":[131],"harming":[132],"performance,":[134],"once":[135],"are":[138],"augmented":[139],"layers.":[142],"hypothesize":[144],"robustness":[147],"consequence":[150],"scale":[153],"invariance":[154],"property":[155],"operators.":[158],"Building":[159],"these":[161],"observations,":[162],"new":[166],"algorithmic":[167],"technique":[168],"under":[174],"very":[175],"low":[176],"budgets":[178,240],"by":[179,203],"sampling":[180],"Gaussian":[183],"distributions":[184],"and":[185,216,235,245],"utilizing":[186],"batch":[187],"or":[188],"layer":[189],"techniques":[191],"prevent":[193],"degradation.":[195],"Our":[196,250],"outperforms":[198],"previous":[199],"approaches,":[200],"including":[201],"DPSGD,":[202],"substantial":[205],"margin":[206],"comprehensive":[209],"set":[210],"experiments":[212],"Computer":[214],"Vision":[215],"Natural":[217],"Language":[218],"Processing":[219],"tasks.":[220],"obtain":[224],"20":[226],"percent":[227],"accuracy":[228],"over":[230],"MNIST":[234],"CIFAR10":[236],"datasets":[237],"DP-privacy":[239],"$\\varepsilon":[242,246],"=":[243,247],"0.05$":[244],"2.0$,":[248],"respectively.":[249],"code":[251],"available":[253],"online:":[254],"https":[256],"URL.":[257]},"counts_by_year":[{"year":2022,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-06-25T00:00:00"}
