{"id":"https://openalex.org/W7156833686","doi":"https://doi.org/10.48550/arxiv.2604.23245","title":"Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption","display_name":"Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7156833686","doi":"https://doi.org/10.48550/arxiv.2604.23245"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23245","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23245","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,"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.2604.23245","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134783365","display_name":"Alexandre Marques","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marques, Alexandre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134796372","display_name":"Beatriz S\u00e1","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S\u00e1, Beatriz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122663199","display_name":"Rui Botelho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Botelho, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134807896","display_name":"Pedro Pinto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pinto, Pedro","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/T10237","display_name":"Cryptography and Data Security","score":0.628000020980835,"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/T10237","display_name":"Cryptography and Data Security","score":0.628000020980835,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.3278000056743622,"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/T14347","display_name":"Big Data and Digital Economy","score":0.004900000058114529,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/encryption","display_name":"Encryption","score":0.8016999959945679},{"id":"https://openalex.org/keywords/homomorphic-encryption","display_name":"Homomorphic encryption","score":0.7583000063896179},{"id":"https://openalex.org/keywords/confidentiality","display_name":"Confidentiality","score":0.43160000443458557},{"id":"https://openalex.org/keywords/client-side-encryption","display_name":"Client-side encryption","score":0.42899999022483826},{"id":"https://openalex.org/keywords/data-security","display_name":"Data security","score":0.3984000086784363},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.36739999055862427},{"id":"https://openalex.org/keywords/functional-encryption","display_name":"Functional encryption","score":0.33489999175071716},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.32919999957084656}],"concepts":[{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.8016999959945679},{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.7583000063896179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753600001335144},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5424000024795532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48350000381469727},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C166501710","wikidata":"https://www.wikidata.org/wiki/Q5132476","display_name":"Client-side encryption","level":4,"score":0.42899999022483826},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.3984000086784363},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781000077724457},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C2780746774","wikidata":"https://www.wikidata.org/wiki/Q17014981","display_name":"Functional encryption","level":4,"score":0.33489999175071716},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C178489894","wikidata":"https://www.wikidata.org/wiki/Q8789","display_name":"Cryptography","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3124000132083893},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2919999957084656},{"id":"https://openalex.org/C61719626","wikidata":"https://www.wikidata.org/wiki/Q17081362","display_name":"Disk encryption hardware","level":5,"score":0.27570000290870667},{"id":"https://openalex.org/C70587473","wikidata":"https://www.wikidata.org/wiki/Q7834111","display_name":"Transformative learning","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C527648132","wikidata":"https://www.wikidata.org/wiki/Q189900","display_name":"Information security","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23245","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23245","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,"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.2604.23245","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23245","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,"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":[{"id":"https://metadata.un.org/sdg/16","score":0.785548210144043,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"use":[1],"of":[2,68,101,166],"Machine":[3],"Learning":[4],"(ML)":[5],"for":[6,84,92,117,154,163],"data-driven":[7],"decision-making":[8],"often":[9],"relies":[10],"on":[11,51,72,110],"access":[12],"to":[13,32,39,137],"sensitive":[14],"datasets,":[15],"which":[16],"introduces":[17],"privacy":[18],"challenges.":[19],"Traditional":[20],"encryption":[21,43,132],"methods":[22],"protect":[23],"data":[24,53,74],"at":[25],"rest":[26],"or":[27],"in":[28,169],"transit":[29],"but":[30],"fail":[31],"secure":[33],"it":[34,38,97],"during":[35],"processing,":[36],"exposing":[37],"unauthorized":[40],"access.":[41],"Homomorphic":[42,131],"emerges":[44],"as":[45,146],"a":[46,82,85,118],"transformative":[47],"solution,":[48],"enabling":[49],"computations":[50],"encrypted":[52,73,111,115],"without":[54],"decryption,":[55],"thus":[56],"preserving":[57],"confidentiality":[58],"throughout":[59],"the":[60,66,99,141,161],"ML":[61,70,168],"pipeline.":[62],"This":[63,158],"paper":[64],"addresses":[65],"challenge":[67],"training":[69,102],"models":[71,109,128],"while":[75],"maintaining":[76],"accuracy":[77],"and":[78,106,113,151],"efficiency":[79],"by":[80],"proposing":[81],"proof-of-concept":[83],"privacy-preserving":[86,167],"framework":[87],"that":[88,127],"leverages":[89],"Cheon-Kim-Kim-Song":[90],"(CKKS)":[91],"approximate":[93],"real-number":[94],"arithmetic.":[95],"Also,":[96],"demonstrates":[98],"feasibility":[100],"K-Nearest":[103],"Neighbors":[104],"(KNN)":[105],"linear":[107],"regression":[108],"data,":[112],"evaluates":[114],"inference":[116],"basic":[119],"Multilayer":[120],"Perceptron":[121],"(MLP)":[122],"architecture.":[123],"Experimental":[124],"results":[125],"show":[126],"trained":[129],"under":[130],"achieve":[133],"performance":[134],"metrics":[135],"comparable":[136],"plaintext-trained":[138],"models,":[139],"validating":[140],"approach.":[142],"However,":[143],"challenges":[144],"such":[145],"computational":[147,175],"overhead,":[148],"noise":[149],"management,":[150],"limited":[152],"support":[153],"non-polynomial":[155],"operations":[156],"persist.":[157],"work":[159],"lays":[160],"groundwork":[162],"broader":[164],"adoption":[165],"real-world":[170],"applications,":[171],"balancing":[172],"security":[173],"with":[174],"feasibility.":[176]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-29T00:00:00"}
