{"id":"https://openalex.org/W7161976983","doi":"https://doi.org/10.48550/arxiv.2605.20521","title":"An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees","display_name":"An exponential mechanism based on quadratic approximations for fine-tuning machine learning models with privacy guarantees","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161976983","doi":"https://doi.org/10.48550/arxiv.2605.20521"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20521","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20521","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.20521","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136691528","display_name":"Hoang Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Hoang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136674293","display_name":"Jorge Ramirez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramirez, Jorge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136725232","display_name":"Jiayi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136702742","display_name":"Alberto Bocchinfuso","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bocchinfuso, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136675856","display_name":"Christopher Stanley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stanley, Christopher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136670514","display_name":"M. Paul Laiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laiu, M. Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.9509999752044678,"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":0.9509999752044678,"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":0.011800000444054604,"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":0.007499999832361937,"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":0.7407000064849854},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.5911999940872192},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5185999870300293},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.4544000029563904},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.4528999924659729},{"id":"https://openalex.org/keywords/randomized-response","display_name":"Randomized response","score":0.44269999861717224},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43689998984336853},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4174000024795532},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.39629998803138733}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.7407000064849854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6438000202178955},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.5911999940872192},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5473999977111816},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5453000068664551},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5185999870300293},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C2776441110","wikidata":"https://www.wikidata.org/wiki/Q1436628","display_name":"Randomized response","level":3,"score":0.44269999861717224},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4174000024795532},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3506999909877777},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33719998598098755},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.3301999866962433},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3239000141620636},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.3125},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C183057437","wikidata":"https://www.wikidata.org/wiki/Q671617","display_name":"Laplace distribution","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2615000009536743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20521","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20521","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:10.48550/arxiv.2605.20521","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20521","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5300313830375671,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-tuning":[0],"adapts":[1],"a":[2,8,38,58,64,86,109],"pretrained":[3,70],"machine":[4],"learning":[5],"model":[6,23,71],"to":[7,25,29,56,117],"small,":[9],"sensitive":[10,31],"dataset,":[11],"but":[12],"this":[13,34],"process":[14],"risks":[15],"memorizing":[16],"individual":[17],"new":[18,76],"data":[19],"points,":[20],"making":[21],"the":[22,43,69,75,114,123,127],"vulnerable":[24],"adversaries":[26],"who":[27],"seek":[28],"extract":[30],"information.":[32],"In":[33],"work,":[35],"we":[36],"develop":[37],"randomized":[39],"algorithm":[40],"based":[41],"on":[42,122],"exponential":[44,80],"mechanism":[45,81],"for":[46,103],"fine-tuning":[47,138],"while":[48],"ensuring":[49],"differential":[50],"privacy.":[51],"Our":[52],"key":[53],"idea":[54],"is":[55],"construct":[57],"simple":[59],"utility":[60],"function":[61],"that":[62,112],"combines":[63],"local":[65],"quadratic":[66],"approximation":[67],"of":[68],"with":[72],"information":[73],"from":[74,85],"dataset.":[77],"The":[78],"resulting":[79],"admits":[82],"exact":[83],"sampling":[84],"multivariate":[87],"normal":[88],"distribution":[89],"in":[90],"closed":[91],"form.":[92],"We":[93,106],"establish":[94],"theoretical":[95],"privacy":[96],"guarantees,":[97],"sensitivity":[98],"bounds,":[99],"and":[100,126],"accuracy":[101],"estimations":[102],"our":[104],"method.":[105],"further":[107],"introduce":[108],"random-projection":[110],"strategy":[111],"makes":[113],"approach":[115],"scalable":[116],"high-dimensional":[118],"models.":[119],"Numerical":[120],"experiments":[121],"MNIST":[124],"benchmark":[125],"MIMIC":[128],"clinical":[129],"dataset":[130],"demonstrate":[131],"competitive":[132],"performance":[133],"against":[134],"existing":[135],"differentially":[136],"private":[137],"techniques.":[139]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}
