{"id":"https://openalex.org/W4385080317","doi":"https://doi.org/10.1109/sp46215.2023.10179409","title":"A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction","display_name":"A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction","publication_year":2023,"publication_date":"2023-05-01","ids":{"openalex":"https://openalex.org/W4385080317","doi":"https://doi.org/10.1109/sp46215.2023.10179409"},"language":"en","primary_location":{"id":"doi:10.1109/sp46215.2023.10179409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sp46215.2023.10179409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Symposium on Security and Privacy (SP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101702107","display_name":"Hanshen Xiao","orcid":"https://orcid.org/0000-0003-3380-4518"},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Hanshen Xiao","raw_affiliation_strings":["MIT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023446377","display_name":"Zihang Xiang","orcid":"https://orcid.org/0009-0008-9352-4810"},"institutions":[{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]},{"id":"https://openalex.org/I71920554","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38","country_code":"SA","type":"education","lineage":["https://openalex.org/I71920554"]}],"countries":["CA","SA"],"is_corresponding":false,"raw_author_name":"Zihang Xiang","raw_affiliation_strings":["KAUST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAUST","institution_ids":["https://openalex.org/I4210099236","https://openalex.org/I71920554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401482","display_name":"Di Wang","orcid":"https://orcid.org/0000-0003-4908-0243"},"institutions":[{"id":"https://openalex.org/I4210099236","display_name":"Kootenay Association for Science & Technology","ror":"https://ror.org/011pv9p44","country_code":"CA","type":"nonprofit","lineage":["https://openalex.org/I4210099236"]},{"id":"https://openalex.org/I71920554","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38","country_code":"SA","type":"education","lineage":["https://openalex.org/I71920554"]}],"countries":["CA","SA"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["KAUST"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAUST","institution_ids":["https://openalex.org/I4210099236","https://openalex.org/I71920554"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000525449","display_name":"Srinivas Devadas","orcid":"https://orcid.org/0000-0001-8253-7714"},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Srinivas Devadas","raw_affiliation_strings":["MIT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.147,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.93442176,"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":"2170","last_page":"2189"},"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":0.9976000189781189,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9700999855995178,"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/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.7395991086959839},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6535534858703613},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.6298779845237732},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.6248930096626282},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5153600573539734},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.46900129318237305},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4660084843635559},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4596512019634247},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.43564945459365845},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40075990557670593},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38847917318344116},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37641382217407227},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2293623983860016}],"concepts":[{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.7395991086959839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6535534858703613},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.6298779845237732},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.6248930096626282},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5153600573539734},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.46900129318237305},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4660084843635559},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4596512019634247},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43564945459365845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40075990557670593},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38847917318344116},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37641382217407227},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2293623983860016},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sp46215.2023.10179409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sp46215.2023.10179409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Symposium on Security and Privacy (SP)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.kaust.edu.sa:10754/693180","is_oa":false,"landing_page_url":"http://hdl.handle.net/10754/693180","pdf_url":null,"source":{"id":"https://openalex.org/S4306401596","display_name":"King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I71920554","host_organization_name":"King Abdullah University of Science and Technology","host_organization_lineage":["https://openalex.org/I71920554"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322320","display_name":"King Abdullah University of Science and Technology","ror":"https://ror.org/01q3tbs38"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":90,"referenced_works":["https://openalex.org/W153185079","https://openalex.org/W168548896","https://openalex.org/W1557833142","https://openalex.org/W1873763122","https://openalex.org/W1985511977","https://openalex.org/W1992926795","https://openalex.org/W2015731569","https://openalex.org/W2051267297","https://openalex.org/W2097583584","https://openalex.org/W2167372639","https://openalex.org/W2184139426","https://openalex.org/W2194775991","https://openalex.org/W2335728318","https://openalex.org/W2473418344","https://openalex.org/W2535690855","https://openalex.org/W2594311007","https://openalex.org/W2652948231","https://openalex.org/W2781626870","https://openalex.org/W2785361959","https://openalex.org/W2884013187","https://openalex.org/W2950943617","https://openalex.org/W2963150697","https://openalex.org/W2963695762","https://openalex.org/W2964199361","https://openalex.org/W2964296660","https://openalex.org/W2970055833","https://openalex.org/W2970408908","https://openalex.org/W2970623506","https://openalex.org/W2998508940","https://openalex.org/W2999251207","https://openalex.org/W3035168593","https://openalex.org/W3035493137","https://openalex.org/W3037261120","https://openalex.org/W3048030262","https://openalex.org/W3112275281","https://openalex.org/W3118608800","https://openalex.org/W3175645569","https://openalex.org/W3186570982","https://openalex.org/W3214167602","https://openalex.org/W4205228770","https://openalex.org/W4221166069","https://openalex.org/W4225150360","https://openalex.org/W4250482878","https://openalex.org/W4283022368","https://openalex.org/W4283705803","https://openalex.org/W4284974526","https://openalex.org/W4286908675","https://openalex.org/W4289117554","https://openalex.org/W4297779039","https://openalex.org/W4297799122","https://openalex.org/W4303438629","https://openalex.org/W4307003864","https://openalex.org/W4385245566","https://openalex.org/W6606855455","https://openalex.org/W6638667902","https://openalex.org/W6677855611","https://openalex.org/W6686137708","https://openalex.org/W6728897251","https://openalex.org/W6743732567","https://openalex.org/W6747417605","https://openalex.org/W6747732332","https://openalex.org/W6749892895","https://openalex.org/W6755310813","https://openalex.org/W6757288671","https://openalex.org/W6762803017","https://openalex.org/W6763249885","https://openalex.org/W6764838729","https://openalex.org/W6766757622","https://openalex.org/W6772129282","https://openalex.org/W6772500962","https://openalex.org/W6779672322","https://openalex.org/W6779737556","https://openalex.org/W6779786081","https://openalex.org/W6779987556","https://openalex.org/W6780226713","https://openalex.org/W6780803235","https://openalex.org/W6781575362","https://openalex.org/W6786871979","https://openalex.org/W6787572546","https://openalex.org/W6789917607","https://openalex.org/W6796410392","https://openalex.org/W6796672819","https://openalex.org/W6799149963","https://openalex.org/W6802187784","https://openalex.org/W6810003945","https://openalex.org/W6810108906","https://openalex.org/W6838846876","https://openalex.org/W6838959450","https://openalex.org/W6839700459","https://openalex.org/W6845813522"],"related_works":["https://openalex.org/W4287755480","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/W3008318776","https://openalex.org/W1977633006","https://openalex.org/W2041416246"],"abstract_inverted_index":{"We":[0,124],"study":[1,188],"the":[2,20,92,104,128,152,155,167,197,217],"bias":[3,129,153],"introduced":[4],"in":[5,61,103,136,253,258],"Differentially-Private":[6],"Stochastic":[7],"Gradient":[8],"Descent":[9],"(DP-SGD)":[10],"with":[11,262,280,291,308],"clipped":[12,84],"or":[13,48,67],"normalized":[14],"per-sample":[15],"gradient.":[16],"As":[17],"one":[18,147],"of":[19,36,65,91,106,122,158,174,219,222,231],"most":[21],"popular":[22],"but":[23],"artificial":[24],"operations":[25],"to":[26,83,98,151,161,170,207,214],"ensure":[27],"bounded":[28],"sensitivity,":[29],"gradient":[30,55,85,132,160,218],"clipping":[31,56,133],"enables":[32],"composite":[33],"privacy":[34,66],"analysis":[35,116],"many":[37,95,254],"iterative":[38],"optimization":[39,142,185],"methods":[40],"without":[41,71],"additional":[42],"assumptions":[43],"on":[44,74,276,305],"either":[45],"learning":[46,198,246],"models":[47],"input":[49],"data.":[50],"Despite":[51],"its":[52],"wide":[53],"applicability,":[54],"also":[57],"presents":[58],"theoretical":[59,115,229],"challenges":[60],"systematically":[62],"instructing":[63],"improvement":[64],"utility.":[68],"In":[69],"general,":[70],"an":[72,184,263],"assumption":[73],"globally-bounded":[75],"gradient,":[76],"classic":[77],"convergence":[78],"analyses":[79],"do":[80],"not":[81],"apply":[82],"descent.":[86],"Further,":[87],"given":[88],"limited":[89],"understanding":[90],"utility":[93],"loss,":[94],"existing":[96],"improvements":[97,175,234,242],"DP-SGD":[99,248],"are":[100],"heuristic,":[101],"especially":[102],"applications":[105],"private":[107,244],"deep":[108,245],"learning.In":[109],"this":[110],"paper,":[111],"we":[112,145,165,187,203,227,272,298],"provide":[113,228],"meaningful":[114],"validated":[117],"by":[118,131],"thorough":[119],"empirical":[120],"results":[121],"DP-SGD.":[123],"point":[125],"out":[126],"that":[127],"caused":[130],"is":[134,154],"underestimated":[135],"previous":[137],"works.":[138],"For":[139,224,256],"generic":[140],"non-convex":[141],"via":[143,235,247],"DP-SGD,":[144],"show":[146],"key":[148],"factor":[149],"contributing":[150],"sampling":[156,177],"noise":[157,178],"stochastic":[159],"be":[162,250],"clipped.":[163],"Accordingly,":[164],"use":[166],"developed":[168],"theory":[169],"build":[171],"a":[172,220,301],"series":[173],"for":[176,287],"reduction":[179,190],"from":[180],"various":[181],"perspectives.":[182],"From":[183],"angle,":[186],"variance":[189],"techniques":[191],"and":[192,212,238,278,284],"propose":[193,204],"inner-outer":[194],"momentum.":[195],"At":[196],"model":[199],"(neural":[200],"network)":[201],"level,":[202],"several":[205],"tricks":[206],"enhance":[208],"network":[209,304],"internal":[210],"normalization":[211,237],"BatchClipping":[213],"carefully":[215],"clip":[216],"batch":[221],"samples.":[223],"data":[225,236,307],"preprocessing,":[226],"justification":[230],"recently":[232],"proposed":[233],"(self-)augmentation.Putting":[239],"these":[240],"systematic":[241],"together,":[243],"can":[249],"significantly":[251],"strengthened":[252],"tasks.":[255],"example,":[257],"computer":[259],"vision":[260],"applications,":[261],"(\u03f5":[264,292],"=":[265,268,293,296],"8,":[266],"\u03b4":[267,295],"10\u22125)":[269],"DP":[270],"guarantee,":[271],"successfully":[273,299],"train":[274,300],"ResNet20":[275],"CIFAR10":[277],"SVHN":[279],"test":[281,309],"accuracy":[282,310],"76.0%":[283],"90.1%,":[285],"respectively;":[286],"natural":[288],"language":[289],"processing,":[290],"4,":[294],"10\u22125),":[297],"recurrent":[302],"neural":[303],"IMDb":[306],"77.5%.":[311]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
