{"id":"https://openalex.org/W3083528917","doi":"https://doi.org/10.2478/popets-2021-0008","title":"Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping","display_name":"Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping","publication_year":2020,"publication_date":"2020-11-09","ids":{"openalex":"https://openalex.org/W3083528917","doi":"https://doi.org/10.2478/popets-2021-0008","mag":"3083528917"},"language":"en","primary_location":{"id":"doi:10.2478/popets-2021-0008","is_oa":true,"landing_page_url":"https://doi.org/10.2478/popets-2021-0008","pdf_url":"https://www.sciendo.com/pdf/10.2478/popets-2021-0008","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.sciendo.com/pdf/10.2478/popets-2021-0008","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100415738","display_name":"Jae Wook Lee","orcid":"https://orcid.org/0000-0002-8756-0195"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaewoo Lee","raw_affiliation_strings":["University of Georgia","*University of Georgia;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Georgia","institution_ids":[]},{"raw_affiliation_string":"*University of Georgia;","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005431144","display_name":"Daniel Kifer","orcid":"https://orcid.org/0000-0002-4611-7066"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Kifer","raw_affiliation_strings":["Penn State University","Penn State Univ.#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Penn State University","institution_ids":["https://openalex.org/I130769515"]},{"raw_affiliation_string":"Penn State Univ.#TAB#","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8055,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79489709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2021","issue":"1","first_page":"128","last_page":"144"},"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":0.9970999956130981,"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":0.9726999998092651,"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":0.8131116628646851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7609522938728333},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.7292605638504028},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6733905076980591},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5749791264533997},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5345404148101807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5298001170158386},{"id":"https://openalex.org/keywords/slowdown","display_name":"Slowdown","score":0.4102065861225128},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40915510058403015},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.38932228088378906},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.275189071893692},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1283462941646576}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.8131116628646851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609522938728333},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.7292605638504028},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6733905076980591},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5749791264533997},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5345404148101807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5298001170158386},{"id":"https://openalex.org/C2777242750","wikidata":"https://www.wikidata.org/wiki/Q31351","display_name":"Slowdown","level":2,"score":0.4102065861225128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40915510058403015},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.38932228088378906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.275189071893692},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1283462941646576},{"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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.2478/popets-2021-0008","is_oa":true,"landing_page_url":"https://doi.org/10.2478/popets-2021-0008","pdf_url":"https://www.sciendo.com/pdf/10.2478/popets-2021-0008","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2009.03106","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.03106","pdf_url":"https://arxiv.org/pdf/2009.03106","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"},{"id":"mag:3083528917","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2009.03106.pdf","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2009.03106","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2009.03106","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.2478/popets-2021-0008","is_oa":true,"landing_page_url":"https://doi.org/10.2478/popets-2021-0008","pdf_url":"https://www.sciendo.com/pdf/10.2478/popets-2021-0008","source":{"id":"https://openalex.org/S4210183172","display_name":"Proceedings on Privacy Enhancing Technologies","issn_l":"2299-0984","issn":["2299-0984"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings on Privacy Enhancing Technologies","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W967544008","https://openalex.org/W1522301498","https://openalex.org/W1530262073","https://openalex.org/W1557833142","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1873763122","https://openalex.org/W1992926795","https://openalex.org/W1994616650","https://openalex.org/W2053637704","https://openalex.org/W2182267394","https://openalex.org/W2194775991","https://openalex.org/W2473418344","https://openalex.org/W2502312327","https://openalex.org/W2520442116","https://openalex.org/W2521757446","https://openalex.org/W2532781556","https://openalex.org/W2535690855","https://openalex.org/W2570467259","https://openalex.org/W2594311007","https://openalex.org/W2605258322","https://openalex.org/W2724212798","https://openalex.org/W2750753921","https://openalex.org/W2784621220","https://openalex.org/W2789226849","https://openalex.org/W2809607392","https://openalex.org/W2896457183","https://openalex.org/W2899771611","https://openalex.org/W2902027132","https://openalex.org/W2908109788","https://openalex.org/W2910283416","https://openalex.org/W2921672033","https://openalex.org/W2927692314","https://openalex.org/W2932329902","https://openalex.org/W2944525415","https://openalex.org/W2947819652","https://openalex.org/W2950943617","https://openalex.org/W2964022804","https://openalex.org/W2995192490","https://openalex.org/W4250482878"],"related_works":["https://openalex.org/W3138854851","https://openalex.org/W3088349136","https://openalex.org/W2905116791","https://openalex.org/W2949892913","https://openalex.org/W3131685502","https://openalex.org/W2962710991","https://openalex.org/W2970294425","https://openalex.org/W2617019596","https://openalex.org/W2902280036","https://openalex.org/W3212271975","https://openalex.org/W2887402482","https://openalex.org/W2952369090","https://openalex.org/W3089416163","https://openalex.org/W3131920822","https://openalex.org/W3128532897","https://openalex.org/W2885097243","https://openalex.org/W2952826183","https://openalex.org/W2976301098","https://openalex.org/W3104896058","https://openalex.org/W3094213821"],"abstract_inverted_index":{"Abstract":[0],"Recent":[1],"work":[2,147],"on":[3],"Renyi":[4],"Differential":[5],"Privacy":[6],"has":[7],"shown":[8],"the":[9,37,49,56,87,96],"feasibility":[10],"of":[11,48,62,89,132,143,151],"applying":[12],"differential":[13],"privacy":[14],"to":[15,51],"deep":[16,25],"learning":[17],"tasks.":[18],"Despite":[19],"their":[20,31],"promise,":[21],"however,":[22],"differentially":[23],"private":[24],"networks":[26],"often":[27,60],"lag":[28],"far":[29],"behind":[30],"non-private":[32,67],"counterparts":[33],"in":[34,42,113,124],"accuracy,":[35],"showing":[36],"need":[38],"for":[39,71,103,136,148],"more":[40],"research":[41,54],"model":[43],"architectures,":[44],"optimizers,":[45],"etc.":[46,162],"One":[47],"barriers":[50],"this":[52,72],"expanded":[53],"is":[55,74],"training":[57,66,91,128,137],"time":[58],"\u2014":[59],"orders":[61],"magnitude":[63],"larger":[64],"than":[65],"networks.":[68],"The":[69],"reason":[70],"slowdown":[73],"a":[75,149],"crucial":[76],"privacy-related":[77],"step":[78],"called":[79],"\u201cper-example":[80],"gradient":[81,105],"clipping\u201d":[82],"whose":[83],"naive":[84],"implementation":[85,123],"undoes":[86],"benefits":[88],"batch":[90,141],"with":[92,110,140],"GPUs.":[93],"By":[94],"analyzing":[95],"back-propagation":[97],"equations":[98],"we":[99],"derive":[100],"new":[101],"methods":[102],"per-example":[104],"clipping":[106],"that":[107],"are":[108],"compatible":[109],"auto-differeniation":[111],"(e.g.,":[112],"Py-Torch":[114],"and":[115,117],"TensorFlow)":[116],"provide":[118],"better":[119],"GPU":[120],"utilization.":[121],"Our":[122],"PyTorch":[125],"showed":[126],"significant":[127],"speed-ups":[129],"(by":[130],"factors":[131],"54x":[133],"-":[134],"94x":[135],"various":[138],"models":[139],"sizes":[142],"128).":[144],"These":[145],"techniques":[146],"variety":[150],"architectural":[152],"choices":[153],"including":[154],"convolutional":[155],"layers,":[156],"recurrent":[157],"networks,":[158],"attention,":[159],"residual":[160],"blocks,":[161]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}