{"id":"https://openalex.org/W4400121218","doi":"https://doi.org/10.1145/3634737.3637680","title":"MOSAIC: A Prune-and-Assemble Approach for Efficient Model Pruning in Privacy-Preserving Deep Learning","display_name":"MOSAIC: A Prune-and-Assemble Approach for Efficient Model Pruning in Privacy-Preserving Deep Learning","publication_year":2024,"publication_date":"2024-06-28","ids":{"openalex":"https://openalex.org/W4400121218","doi":"https://doi.org/10.1145/3634737.3637680"},"language":"en","primary_location":{"id":"doi:10.1145/3634737.3637680","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3634737.3637680","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3634737.3637680","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068070082","display_name":"Yifei Cai","orcid":"https://orcid.org/0000-0002-1372-656X"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifei Cai","raw_affiliation_strings":["Old Dominion University, Norfolk, VA, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-1372-656X","affiliations":[{"raw_affiliation_string":"Old Dominion University, Norfolk, VA, United States of America","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357529","display_name":"Qiao Zhang","orcid":"https://orcid.org/0000-0002-7752-0528"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiao Zhang","raw_affiliation_strings":["Chongqing University, Chongqin, China"],"raw_orcid":"https://orcid.org/0000-0002-7752-0528","affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqin, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061056153","display_name":"Rui Ning","orcid":"https://orcid.org/0000-0003-4050-6252"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Ning","raw_affiliation_strings":["Old Dominion University, Norfolk, VA, USA"],"raw_orcid":"https://orcid.org/0000-0003-4050-6252","affiliations":[{"raw_affiliation_string":"Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074327315","display_name":"Chunsheng Xin","orcid":"https://orcid.org/0000-0001-5575-2849"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunsheng Xin","raw_affiliation_strings":["Old Dominion University, Norfolk, VA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5575-2849","affiliations":[{"raw_affiliation_string":"Old Dominion University, Norfolk, VA, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101816294","display_name":"Hongyi Wu","orcid":"https://orcid.org/0000-0002-6112-9313"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongyi Wu","raw_affiliation_strings":["University of Arizona, Tucson, AZ, United States of America"],"raw_orcid":"https://orcid.org/0000-0002-6112-9313","affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, AZ, United States of America","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1034","last_page":"1048"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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/T10237","display_name":"Cryptography and Data Security","score":0.9994000196456909,"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":0.9972000122070312,"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/pruning","display_name":"Pruning","score":0.8512884378433228},{"id":"https://openalex.org/keywords/mosaic","display_name":"Mosaic","score":0.8040381669998169},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7925307750701904},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5718695521354675},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45248889923095703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3258403539657593}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8512884378433228},{"id":"https://openalex.org/C110739175","wikidata":"https://www.wikidata.org/wiki/Q133067","display_name":"Mosaic","level":2,"score":0.8040381669998169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7925307750701904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5718695521354675},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45248889923095703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3258403539657593},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3634737.3637680","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3634737.3637680","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},{"id":"pmh:oai:digitalcommons.odu.edu:computerscience_fac_pubs-1337","is_oa":true,"landing_page_url":"https://digitalcommons.odu.edu/computerscience_fac_pubs/332","pdf_url":null,"source":{"id":"https://openalex.org/S4377196314","display_name":"ODU Digital Commons (Old Dominion University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I81365321","host_organization_name":"Old Dominion University","host_organization_lineage":["https://openalex.org/I81365321"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computer Science Faculty Publications","raw_type":"conference"}],"best_oa_location":{"id":"doi:10.1145/3634737.3637680","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3634737.3637680","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G146915904","display_name":null,"funder_award_id":"DUE-2153358","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3058528167","display_name":null,"funder_award_id":"OAC-2320999","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5956359401","display_name":null,"funder_award_id":"CNS-2120279","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7517489523","display_name":null,"funder_award_id":"CNS-2153358","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1874619058","https://openalex.org/W1969009977","https://openalex.org/W2096733369","https://openalex.org/W2112796928","https://openalex.org/W2132172731","https://openalex.org/W2141420453","https://openalex.org/W2163605009","https://openalex.org/W2170696315","https://openalex.org/W2194775991","https://openalex.org/W2226167778","https://openalex.org/W2620512600","https://openalex.org/W2701059868","https://openalex.org/W2765200655","https://openalex.org/W2768174108","https://openalex.org/W2808706611","https://openalex.org/W2889746123","https://openalex.org/W2912554321","https://openalex.org/W2962851801","https://openalex.org/W2963000224","https://openalex.org/W2963106566","https://openalex.org/W2963752132","https://openalex.org/W3016063723","https://openalex.org/W3093021001","https://openalex.org/W3104454153","https://openalex.org/W3177656211","https://openalex.org/W4205350912","https://openalex.org/W4299301436","https://openalex.org/W6725543821"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"To":[0,36,82],"enable":[1],"common":[2],"users":[3],"to":[4,33,70,101,142,195,235,258],"capitalize":[5],"on":[6,205,224,250],"the":[7,22,34,38,47,51,64,74,84,120,128,150,153,161,184,188,229,237,253,263],"power":[8],"of":[9,30,41,50,67,87],"deep":[10,27,88,109],"learning,":[11],"Machine":[12],"Learning":[13],"as":[14,44,46,97,123,200,209],"a":[15,98,112,137,175],"Service":[16],"(MLaaS)":[17],"has":[18,94],"been":[19,60,95],"proposed":[20],"in":[21,244,248],"literature,":[23],"which":[24],"opens":[25],"powerful":[26],"learning":[28,89],"models":[29,198],"service":[31],"providers":[32],"public.":[35],"protect":[37],"data":[39],"privacy":[40,49],"end":[42],"users,":[43],"well":[45],"model":[48,92,129],"server,":[52],"several":[53],"state-of-the-art":[54],"privacy-preserving":[55],"MLaaS":[56,264],"frameworks":[57,69,265],"have":[58],"also":[59],"proposed.":[61],"Nevertheless,":[62],"despite":[63],"exquisite":[65],"design":[66],"these":[68],"enhance":[71],"computation":[72,85],"efficiency,":[73,122],"computational":[75],"cost":[76,239,255],"remains":[77],"expensive":[78],"for":[79,107],"practical":[80,108],"applications.":[81],"improve":[83],"efficiency":[86],"(DL)":[90],"models,":[91,226],"pruning":[93,115,121,140,149,157,177,189,223],"adopted":[96],"strategic":[99],"approach":[100],"remarkably":[102],"compress":[103],"DL":[104,197],"models.":[105],"However,":[106],"neural":[110],"networks,":[111],"problem":[113,185],"called":[114],"structure":[116,190],"inflation":[117],"significantly":[118,227],"limits":[119],"it":[124],"can":[125],"seriously":[126],"hurt":[127],"accuracy.":[130,245],"In":[131],"this":[132,144],"paper,":[133],"we":[134],"propose":[135],"MOSAIC,":[136],"highly":[138],"flexible":[139],"framework,":[141],"address":[143],"critical":[145],"challenge.":[146],"By":[147],"first":[148],"network":[151],"with":[152],"carefully":[154],"selected":[155],"basic":[156],"units,":[158],"then":[159],"assembling":[160],"pruned":[162],"units":[163],"into":[164],"suitable":[165],"HE":[166],"Pruning":[167],"Structures":[168],"through":[169],"smart":[170],"channel":[171],"transformations,":[172],"MOSAIC":[173,194,218],"achieves":[174],"high":[176],"ratio":[178],"while":[179],"avoiding":[180],"accuracy":[181],"reduction,":[182],"eliminating":[183],"plagued":[186],"by":[187],"inflation.":[191],"We":[192],"apply":[193],"popular":[196],"such":[199,208],"VGG":[201],"and":[202,211,220,232,260,267],"ResNet":[203],"series":[204],"classic":[206],"datasets":[207],"CIFAR-10":[210],"Tiny":[212,251],"ImageNet.":[213],"Experimental":[214],"results":[215],"demonstrate":[216],"that":[217],"effectively":[219],"flexibly":[221],"conducts":[222],"those":[225],"reducing":[228],"Perm,":[230],"Mult,":[231],"Add":[233],"operations":[234],"achieve":[236],"global":[238],"reduction":[240],"without":[241],"any":[242],"loss":[243],"For":[246],"instance,":[247],"VGG-16":[249],"ImageNet,":[252],"total":[254],"is":[256],"reduced":[257],"21.14%":[259],"29.49%":[261],"under":[262],"GAZELLE":[266],"CrypTFlow2,":[268],"respectively.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
