{"id":"https://openalex.org/W3047904087","doi":"https://doi.org/10.1145/3404397.3404399","title":"ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs","display_name":"ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs","publication_year":2020,"publication_date":"2020-08-09","ids":{"openalex":"https://openalex.org/W3047904087","doi":"https://doi.org/10.1145/3404397.3404399","mag":"3047904087"},"language":"en","primary_location":{"id":"doi:10.1145/3404397.3404399","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404397.3404399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"49th International Conference on Parallel Processing - ICPP","raw_type":"proceedings-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/A5116225873","display_name":"Chen Zheng","orcid":"https://orcid.org/0009-0000-8342-9504"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zheng Chen","raw_affiliation_strings":["Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091139467","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0003-1983-7321"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015692437","display_name":"Amelie Chi Zhou","orcid":"https://orcid.org/0000-0001-8343-2367"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Amelie Chi Zhou","raw_affiliation_strings":["Shenzhen University"],"affiliations":[{"raw_affiliation_string":"Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071200777","display_name":"Jidong Zhai","orcid":"https://orcid.org/0000-0002-7656-6428"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jidong Zhai","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100337319","display_name":"Chenyang Zhang","orcid":"https://orcid.org/0000-0002-7627-6359"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Zhang","raw_affiliation_strings":["Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008721449","display_name":"Xiaoyong Du","orcid":"https://orcid.org/0000-0002-5757-9135"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyong Du","raw_affiliation_strings":["Renmin University of China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5116225873"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.5302,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7265667,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9979000091552734,"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":0.9977999925613403,"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/computer-science","display_name":"Computer science","score":0.8907876014709473},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7111308574676514},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6071783304214478},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5430260896682739},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5418956279754639},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5181013941764832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4806986451148987},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47093406319618225},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.42956677079200745},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3872227370738983},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.36959636211395264},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1663840413093567},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1531205177307129}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8907876014709473},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7111308574676514},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6071783304214478},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5430260896682739},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5418956279754639},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5181013941764832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4806986451148987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47093406319618225},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.42956677079200745},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3872227370738983},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.36959636211395264},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1663840413093567},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1531205177307129},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3404397.3404399","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404397.3404399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"49th International Conference on Parallel Processing - ICPP","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W104209573","https://openalex.org/W179875071","https://openalex.org/W1536771657","https://openalex.org/W1663973292","https://openalex.org/W1919818376","https://openalex.org/W1967501281","https://openalex.org/W1969991578","https://openalex.org/W1971991172","https://openalex.org/W2003668524","https://openalex.org/W2010756295","https://openalex.org/W2054522323","https://openalex.org/W2109426455","https://openalex.org/W2128268549","https://openalex.org/W2144354855","https://openalex.org/W2155893237","https://openalex.org/W2171928131","https://openalex.org/W2267666116","https://openalex.org/W2330308866","https://openalex.org/W2473418344","https://openalex.org/W2564237233","https://openalex.org/W2605516844","https://openalex.org/W2606722458","https://openalex.org/W2701059868","https://openalex.org/W2761705332","https://openalex.org/W2887020706","https://openalex.org/W2889210204","https://openalex.org/W2895865029","https://openalex.org/W2900413068","https://openalex.org/W2942443375","https://openalex.org/W2951568391","https://openalex.org/W2967987264","https://openalex.org/W2970031065","https://openalex.org/W2972586567","https://openalex.org/W2984644945","https://openalex.org/W2990407694","https://openalex.org/W3013424323","https://openalex.org/W3029558105","https://openalex.org/W3098059246","https://openalex.org/W4249965265","https://openalex.org/W4252637763","https://openalex.org/W4299301436"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2983282793","https://openalex.org/W1973046741"],"abstract_inverted_index":{"Machine":[0],"learning":[1,45,72,81,88,120,192],"has":[2,49,101],"been":[3,15,50,102],"widely":[4],"used":[5],"in":[6,133],"our":[7],"daily":[8],"lives.":[9],"Large":[10],"amounts":[11],"of":[12,38,66,117,129,159,203],"data":[13,27,140,149],"have":[14],"continuously":[16],"produced":[17],"and":[18,26,144,146,174],"transmitted":[19],"to":[20,34,52,61,113,195],"the":[21,36,39,62,69,78,98,115,134,187,196],"cloud":[22],"for":[23,170,177],"model":[24],"training":[25],"processing,":[28],"which":[29],"raises":[30],"a":[31,42,109,157],"problem:":[32],"how":[33],"preserve":[35],"security":[37],"data.":[40],"Recently,":[41],"secure":[43,70,86,118,190],"machine":[44,71,80,87,119,191],"system":[46],"named":[47],"SecureML":[48],"proposed":[51],"solve":[53],"this":[54,105,185],"issue":[55],"using":[56],"two-party":[57,67,124],"computation.":[58,125],"However,":[59],"due":[60],"excessive":[63],"computation":[64,136],"expenses":[65],"computation,":[68],"is":[73,186],"about":[74],"2x":[75],"slower":[76],"than":[77],"original":[79],"methods.":[82],"Previous":[83],"work":[84],"on":[85,91,123],"mostly":[89],"focused":[90],"novel":[92,160],"protocols":[93],"or":[94],"improving":[95],"accuracy,":[96],"while":[97],"performance":[99,116],"metric":[100],"ignored.":[103],"In":[104],"paper,":[106],"we":[107,155,183],"propose":[108,156],"GPU-based":[110,189],"framework":[111],"ParSecureML":[112,131,199,206],"improve":[114],"algorithms":[121],"based":[122],"The":[126],"main":[127],"challenges":[128],"developing":[130],"lie":[132],"complex":[135],"patterns,":[137],"frequent":[138],"intra-node":[139,171],"transmission":[141,176],"between":[142],"CPU":[143],"GPU,":[145],"complicated":[147],"inter-node":[148,178],"dependence.":[150],"To":[151],"handle":[152],"these":[153],"challenges,":[154],"series":[158],"solutions,":[161],"including":[162],"profiling-guided":[163],"adaptive":[164],"GPU":[165],"utilization,":[166],"fine-grained":[167],"double":[168],"pipeline":[169],"CPU-GPU":[172],"cooperation,":[173],"compressed":[175],"communication.":[179],"As":[180],"far":[181],"as":[182],"know,":[184],"first":[188],"framework.":[193],"Compared":[194],"state-of-the-art":[197],"framework,":[198],"achieves":[200],"an":[201],"average":[202],"32.2x":[204],"speedup.":[205],"can":[207],"be":[208],"downloaded":[209],"from":[210],"https://github.com/ZhengChenCS/ParSecureML.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-02-25T23:00:34.991745","created_date":"2025-10-10T00:00:00"}
