乐胖代购免代理版

{"id":"https://openalex.org/W2985527074","doi":"https://doi.org/10.1145/3319535.3363207","title":"Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference","display_name":"Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference","publication_year":2019,"publication_date":"2019-11-06","ids":{"openalex":"https://openalex.org/W2985527074","doi":"https://doi.org/10.1145/3319535.3363207","mag":"2985527074"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3319535.3363207","pdf_url":null,"source":{"id":"https://openalex.org/S4363608815","display_name":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100353665","display_name":"Hao Chen","orcid":"https://orcid.org/0000-0003-4457-6231"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101595453","display_name":"Wei Dai","orcid":"https://orcid.org/0000-0002-3542-8889"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Dai","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091504641","display_name":"Miran Kim","orcid":"https://orcid.org/0000-0003-3564-6090"},"institutions":[{"id":"https://openalex.org/I919571938","display_name":"The University of Texas Health Science Center at Houston","ror":"https://ror.org/03gds6c39","country_code":"US","type":"education","lineage":["https://openalex.org/I919571938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miran Kim","raw_affiliation_strings":["UT Health Science Center at Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"UT Health Science Center at Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I919571938"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101119576","display_name":"Yongsoo Song","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongsoo Song","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.172,"has_fulltext":false,"cited_by_count":165,"citation_normalized_percentile":{"value":0.999908,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9999,"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":0.9999,"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.9938,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9767,"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":0.93066347},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4166643}],"concepts":[{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.93066347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71545774},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.70446855},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.52721906},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.50961185},{"id":"https://openalex.org/C6295992","wikidata":"https://www.wikidata.org/wiki/Q976521","display_name":"Cryptosystem","level":3,"score":0.4480543},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44119143},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4166643},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3282196},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.14490962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11589882},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.094641626},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3319535.3363207","pdf_url":null,"source":{"id":"https://openalex.org/S4363608815","display_name":"Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320308129","funder_display_name":"Cancer Prevention and Research Institute of Texas","award_id":"RR180012"},{"funder":"https://openalex.org/F4320337373","funder_display_name":"Center for Information Technology","award_id":"U01TR002062, R01GM118574, R01GM124111, R41HG010978"}],"datasets":[],"versions":[],"referenced_works_count":46,"referenced_works":["https://openalex.org/W1635361314","https://openalex.org/W1755636270","https://openalex.org/W1850468005","https://openalex.org/W1966731635","https://openalex.org/W2006453614","https://openalex.org/W2031533839","https://openalex.org/W2048994663","https://openalex.org/W2088492763","https://openalex.org/W2152926062","https://openalex.org/W2226167778","https://openalex.org/W2271840356","https://openalex.org/W2296488250","https://openalex.org/W236632755","https://openalex.org/W2400700555","https://openalex.org/W2401959250","https://openalex.org/W2435473771","https://openalex.org/W2499150512","https://openalex.org/W2499340481","https://openalex.org/W2527617455","https://openalex.org/W2538296643","https://openalex.org/W2554750353","https://openalex.org/W2701059868","https://openalex.org/W2765200655","https://openalex.org/W2766141585","https://openalex.org/W2766831133","https://openalex.org/W2766917641","https://openalex.org/W2768174108","https://openalex.org/W2783769334","https://openalex.org/W2794634409","https://openalex.org/W2794685135","https://openalex.org/W2794927157","https://openalex.org/W2794974431","https://openalex.org/W2795187181","https://openalex.org/W2801958627","https://openalex.org/W2889746123","https://openalex.org/W2895782209","https://openalex.org/W2896938420","https://openalex.org/W2899140612","https://openalex.org/W2903536544","https://openalex.org/W2942445168","https://openalex.org/W2943497929","https://openalex.org/W2978337724","https://openalex.org/W3028867652","https://openalex.org/W56544557","https://openalex.org/W774911181","https://openalex.org/W913176383"],"related_works":["https://openalex.org/W972276598","https://openalex.org/W4321353415","https://openalex.org/W4313300189","https://openalex.org/W3012147850","https://openalex.org/W2993631497","https://openalex.org/W2949835517","https://openalex.org/W2625655658","https://openalex.org/W2539930818","https://openalex.org/W2378211422","https://openalex.org/W2130974462"],"abstract_inverted_index":{"Homomorphic":[0,26],"Encryption":[1,27],"(HE)":[2],"is":[3,30,110],"a":[4,19,92,126,138,151,159],"cryptosystem":[5],"which":[6,29,61],"supports":[7],"computation":[8,145],"on":[9,36,191],"encrypted":[10,38,170,193],"data.":[11],"L\u00f3":[12],"pez-Alt":[13],"et":[14,71],"al.":[15,72],"(STOC":[16],"2012)":[17],"proposed":[18],"generalized":[20],"notion":[21],"of":[22,32,49,95,107,141],"HE,":[23],"called":[24],"Multi-Key":[25],"(MKHE),":[28],"capable":[31],"performing":[33],"arithmetic":[34],"operations":[35],"ciphertexts":[37,119],"under":[39,171],"different":[40,172],"keys.":[41,173],"In":[42],"this":[43],"paper,":[44],"we":[45,153],"present":[46,57],"multi-key":[47,85,115],"variants":[48],"two":[50,187],"HE":[51,82],"schemes":[52,98,136],"with":[53,121],"packed":[54],"ciphertexts.":[55],"We":[56,75,90],"new":[58],"relinearization":[59,127],"algorithms":[60],"are":[62,169],"simpler":[63],"and":[64,167],"faster":[65],"than":[66],"previous":[67],"method":[68],"by":[69,125,186],"Chen":[70],"(TCC":[73],"2017).":[74],"then":[76],"generalize":[77],"the":[78,105,196],"bootstrapping":[79],"techniques":[80],"for":[81],"to":[83,180],"obtain":[84],"fully":[86,188],"homomorphic":[87,112],"encryption":[88],"schemes.":[89],"provide":[91],"proof-of-concept":[93],"implementation":[94,175],"both":[96],"MKHE":[97,135],"using":[99,158],"Microsoft":[100],"SEAL.":[101],"For":[102],"example,":[103],"when":[104],"dimension":[106],"base":[108],"ring":[109],"8192,":[111],"multiplication":[113],"between":[114,146],"BFV":[116],"(resp.":[117,131],"CKKS)":[118],"associated":[120],"four":[122],"parties":[123],"followed":[124,185],"takes":[128,176],"about":[129,177],"116":[130],"67)":[132],"milliseconds.":[133],"Our":[134,174],"have":[137],"wide":[139],"range":[140],"applications":[142],"in":[143],"secure":[144],"multiple":[147],"data":[148,166],"providers.":[149],"As":[150],"benchmark,":[152],"homomorphically":[154],"classify":[155],"an":[156,192],"image":[157,194],"pre-trained":[160],"neural":[161],"network":[162],"model,":[163],"where":[164],"input":[165],"model":[168],"1.8":[178],"seconds":[179],"evaluate":[181],"one":[182],"convolutional":[183],"layer":[184],"connected":[189],"layers":[190],"from":[195],"MNIST":[197],"dataset.":[198]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2985527074","counts_by_year":[{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":40},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":5}],"updated_date":"2025-01-07T06:13:08.286471","created_date":"2019-11-22"}