{"id":"https://openalex.org/W7128674031","doi":"https://doi.org/10.48550/arxiv.2602.10584","title":"When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning","display_name":"When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128674031","doi":"https://doi.org/10.48550/arxiv.2602.10584"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10584","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125763722","display_name":"Mohammad Partohaghighi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Partohaghighi, Mohammad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005703929","display_name":"Roummel F. Marcia","orcid":"https://orcid.org/0000-0001-6838-140X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcia, Roummel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125725320","display_name":"Bruce J. West","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"West, Bruce J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100715957","display_name":"YangQuan Chen","orcid":"https://orcid.org/0000-0002-7422-5988"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, YangQuan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5125763722"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":0.8375999927520752,"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":0.8375999927520752,"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":0.11230000108480453,"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":0.014299999922513962,"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/clipping","display_name":"Clipping (morphology)","score":0.7728999853134155},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5870000123977661},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.5144000053405762},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.44600000977516174},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.43619999289512634},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.40860000252723694},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.39089998602867126},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3788999915122986},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.35920000076293945},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.35839998722076416}],"concepts":[{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.7728999853134155},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5870000123977661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5662000179290771},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4392000138759613},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.40860000252723694},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3788999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3750999867916107},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.35920000076293945},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C107464732","wikidata":"https://www.wikidata.org/wiki/Q235781","display_name":"Adaptive control","level":3,"score":0.3386000096797943},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3084000051021576},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.29249998927116394},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2727000117301941},{"id":"https://openalex.org/C193519340","wikidata":"https://www.wikidata.org/wiki/Q891179","display_name":"Data loss","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10584","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.10584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10584","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.10584","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4559505879878998,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Privacy-preserving":[0],"training":[1,108],"on":[2,7,51],"sensitive":[3],"data":[4],"commonly":[5],"relies":[6],"differentially":[8],"private":[9],"stochastic":[10],"optimization":[11,33],"with":[12,107],"gradient":[13,53],"clipping":[14,19,47,70,126],"and":[15,42,59,64,100,116,149],"Gaussian":[16],"noise.":[17],"The":[18],"threshold":[20,75,127,145],"is":[21,112],"a":[22,68,77,93,102,119],"critical":[23],"control":[24],"knob:":[25],"if":[26,35],"set":[27],"too":[28,36],"small,":[29],"systematic":[30],"over-clipping":[31],"induces":[32],"bias;":[34],"large,":[37],"injected":[38],"noise":[39],"dominates":[40],"updates":[41,124,146],"degrades":[43],"accuracy.":[44],"Existing":[45],"adaptive":[46],"methods":[48],"often":[49],"depend":[50],"per-example":[52],"norm":[54],"statistics,":[55],"adding":[56],"computational":[57],"overhead":[58],"introducing":[60],"sensitivity":[61],"to":[62],"datasets":[63],"architectures.":[65],"We":[66],"propose":[67],"control-driven":[69],"strategy":[71],"that":[72,123,156],"adapts":[73],"the":[74,90,125,130,134,143,158],"using":[76],"lightweight,":[78],"weight-only":[79],"spectral":[80,98,104],"diagnostic":[81],"computed":[82],"from":[83],"model":[84],"parameters.":[85],"At":[86],"periodic":[87],"probe":[88],"steps,":[89],"method":[91],"analyzes":[92],"designated":[94],"weight":[95],"matrix":[96],"via":[97],"decomposition":[99],"estimates":[101],"heavy-tailed":[103],"indicator":[105,111],"associated":[106],"stability.":[109],"This":[110],"smoothed":[113],"over":[114],"time":[115],"fed":[117],"into":[118],"bounded":[120],"feedback":[121],"controller":[122,135],"multiplicatively":[128],"in":[129],"log":[131],"domain.":[132],"Because":[133],"uses":[136],"only":[137],"parameters":[138],"produced":[139],"during":[140],"privacy-preserving":[141],"training,":[142],"resulting":[144],"are":[147],"post-processing":[148],"do":[150],"not":[151],"increase":[152],"privacy":[153],"loss":[154],"beyond":[155],"of":[157],"underlying":[159],"DP":[160],"optimizer":[161],"under":[162],"standard":[163],"composition":[164],"accounting.":[165]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-13T00:00:00"}
