{"id":"https://openalex.org/W4361986437","doi":"https://doi.org/10.1109/tifs.2023.3263631","title":"Optimized Privacy-Preserving CNN Inference With Fully Homomorphic Encryption","display_name":"Optimized Privacy-Preserving CNN Inference With Fully Homomorphic Encryption","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4361986437","doi":"https://doi.org/10.1109/tifs.2023.3263631"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2023.3263631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2023.3263631","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-article"},"type":"article","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/A5100357238","display_name":"Dongwoo Kim","orcid":"https://orcid.org/0000-0001-5780-7110"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongwoo Kim","raw_affiliation_strings":["College of AI Convergence, Dongguk University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-5780-7110","affiliations":[{"raw_affiliation_string":"College of AI Convergence, Dongguk University, Seoul, South Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016738612","display_name":"Cyril Guyot","orcid":"https://orcid.org/0000-0002-8336-9537"},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cyril Guyot","raw_affiliation_strings":["Western Digital Research, Milpitas, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8336-9537","affiliations":[{"raw_affiliation_string":"Western Digital Research, Milpitas, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.8045,"has_fulltext":false,"cited_by_count":105,"citation_normalized_percentile":{"value":0.99277363,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"18","issue":null,"first_page":"2175","last_page":"2187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991999864578247,"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":0.9937999844551086,"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/homomorphic-encryption","display_name":"Homomorphic encryption","score":0.9335988759994507},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8857300877571106},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7435844540596008},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7275283932685852},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.5984386801719666},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5657390356063843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5422476530075073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5138897895812988},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.48341381549835205},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.4610620439052582},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43631571531295776},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4042249321937561},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.17558032274246216}],"concepts":[{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.9335988759994507},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8857300877571106},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7435844540596008},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7275283932685852},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.5984386801719666},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5657390356063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5422476530075073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5138897895812988},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.48341381549835205},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.4610620439052582},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43631571531295776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4042249321937561},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.17558032274246216},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tifs.2023.3263631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2023.3263631","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4277743677","display_name":null,"funder_award_id":"S-2022-G0001-00070","funder_id":"https://openalex.org/F4320321213","funder_display_name":"Dongguk University"}],"funders":[{"id":"https://openalex.org/F4320321213","display_name":"Dongguk University","ror":"https://ror.org/057q6n778"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W150223756","https://openalex.org/W1979120705","https://openalex.org/W2027471022","https://openalex.org/W2031533839","https://openalex.org/W2117539524","https://openalex.org/W2177209050","https://openalex.org/W2194775991","https://openalex.org/W2435473771","https://openalex.org/W2554750353","https://openalex.org/W2768174108","https://openalex.org/W2768505000","https://openalex.org/W2794888826","https://openalex.org/W2794974431","https://openalex.org/W2897925395","https://openalex.org/W2899140612","https://openalex.org/W2901469102","https://openalex.org/W2903536544","https://openalex.org/W2955401130","https://openalex.org/W2964137095","https://openalex.org/W2969350772","https://openalex.org/W2987932087","https://openalex.org/W2990280959","https://openalex.org/W3003331030","https://openalex.org/W3003532255","https://openalex.org/W3006531732","https://openalex.org/W3028867652","https://openalex.org/W3033405216","https://openalex.org/W3034945632","https://openalex.org/W3035490021","https://openalex.org/W3093021001","https://openalex.org/W3094696138","https://openalex.org/W3102723029","https://openalex.org/W3104454153","https://openalex.org/W3118608800","https://openalex.org/W3127843656","https://openalex.org/W3141585064","https://openalex.org/W3173128495","https://openalex.org/W3195598474","https://openalex.org/W3200754280","https://openalex.org/W3214408283","https://openalex.org/W4205474236","https://openalex.org/W4290802460","https://openalex.org/W6606067566","https://openalex.org/W6748082217","https://openalex.org/W6756592279","https://openalex.org/W6757619422","https://openalex.org/W6767709616","https://openalex.org/W6778434676","https://openalex.org/W6785571875","https://openalex.org/W6787972765","https://openalex.org/W7067201269"],"related_works":["https://openalex.org/W1534274833","https://openalex.org/W3117246195","https://openalex.org/W156620619","https://openalex.org/W2152926062","https://openalex.org/W2949607150","https://openalex.org/W2950312267","https://openalex.org/W3123945077","https://openalex.org/W2398715209","https://openalex.org/W3203896436","https://openalex.org/W913176383"],"abstract_inverted_index":{"Inference":[0],"of":[1,78,89,120,127,138],"machine":[2],"learning":[3],"models":[4],"with":[5,81,105,146,163],"data":[6,40],"privacy":[7,14,41],"guarantees":[8],"has":[9,33],"been":[10],"widely":[11],"studied":[12],"as":[13],"concerns":[15],"are":[16],"getting":[17],"growing":[18],"attention":[19],"from":[20,67],"the":[21,62,84,90,166,170],"community.":[22],"Among":[23],"others,":[24],"secure":[25],"inference":[26,160],"based":[27],"on":[28,98,129,161],"Fully":[29],"Homomorphic":[30],"Encryption":[31],"(FHE)":[32],"proven":[34],"its":[35],"utility":[36],"by":[37],"providing":[38],"stringent":[39],"at":[42,110,156],"sometimes":[43],"affordable":[44],"cost.":[45],"Still,":[46],"previous":[47],"work":[48],"was":[49],"restricted":[50],"to":[51,61],"shallow":[52],"and":[53,57,150],"narrow":[54],"neural":[55],"networks":[56],"simple":[58],"tasks":[59],"due":[60],"high":[63],"computational":[64],"cost":[65,85],"incurred":[66],"FHE.":[68],"In":[69],"this":[70],"paper,":[71],"we":[72,108,154],"propose":[73],"a":[74,125],"more":[75],"efficient":[76],"way":[77],"evaluating":[79,144],"convolutions":[80],"FHE,":[82],"where":[83],"remains":[86],"constant":[87],"regardless":[88],"kernel":[91,100],"size,":[92],"resulting":[93],"in":[94,117,136],"12\u201346\u00d7":[95],"timing":[96,115],"improvement":[97],"various":[99],"sizes.":[101],"Combining":[102],"our":[103,139],"methods":[104,140],"FHE":[106,164],"bootstrapping,":[107],"achieve":[109,155],"least":[111,157],"18.9%":[112],"(and":[113,124,131],"48.1%)":[114],"reduction":[116],"homomorphic":[118],"evaluation":[119],"20-layer":[121],"CNN":[122],"classifiers":[123],"part":[126],"it)":[128],"CIFAR10/100":[130,162],"ImageNet,":[132],"respectively)":[133],"datasets.":[134],"Furthermore,":[135],"consideration":[137],"being":[141],"effective":[142],"for":[143],"CNNs":[145],"intensive":[147],"convolutional":[148],"operations":[149],"exploring":[151],"such":[152],"CNNs,":[153],"5\u00d7":[158],"faster":[159],"than":[165],"prior":[167],"works":[168],"having":[169],"same":[171],"or":[172],"less":[173],"accuracy.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":52},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":4}],"updated_date":"2026-07-25T15:57:00.446498","created_date":"2025-10-10T00:00:00"}
