{"id":"https://openalex.org/W3006704755","doi":"https://doi.org/10.1109/bigdata47090.2019.9006000","title":"SecureGBM: Secure Multi-Party Gradient Boosting","display_name":"SecureGBM: Secure Multi-Party Gradient Boosting","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3006704755","doi":"https://doi.org/10.1109/bigdata47090.2019.9006000","mag":"3006704755"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5026509593","display_name":"Zhi Feng","orcid":"https://orcid.org/0009-0003-5407-0629"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhi Feng","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081254155","display_name":"Haoyi Xiong","orcid":"https://orcid.org/0000-0002-5451-3253"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyi Xiong","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082216581","display_name":"Chuanyuan Song","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanyuan Song","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101583047","display_name":"Sijia Yang","orcid":"https://orcid.org/0009-0000-2721-3952"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijia Yang","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]},{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012069944","display_name":"Baoxin Zhao","orcid":"https://orcid.org/0000-0002-3222-539X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoxin Zhao","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100736219","display_name":"Licheng Wang","orcid":"https://orcid.org/0000-0001-8418-1897"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Wang","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100662054","display_name":"Zeyu Chen","orcid":"https://orcid.org/0000-0001-6286-0581"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyu Chen","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087563919","display_name":"Shengwen Yang","orcid":"https://orcid.org/0000-0001-8774-187X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengwen Yang","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]},{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100352447","display_name":"Liping Liu","orcid":"https://orcid.org/0000-0002-8467-5210"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Liu","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080409305","display_name":"Jun Huan","orcid":"https://orcid.org/0000-0002-7020-1604"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Huan","raw_affiliation_strings":["Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Big Data Group (BDG), Big Data Lab (BDL) and, PaddlePaddle (DLTP), Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5026509593"],"corresponding_institution_ids":["https://openalex.org/I98301712"],"apc_list":null,"apc_paid":null,"fwci":3.7538,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.94781121,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1312","last_page":"1321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","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/T10237","display_name":"Cryptography and Data Security","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/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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9987000226974487,"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":0.8487282395362854},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6769121289253235},{"id":"https://openalex.org/keywords/homomorphic-encryption","display_name":"Homomorphic encryption","score":0.6529179811477661},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6224391460418701},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.507532000541687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48270726203918457},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.47518932819366455},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.4683491587638855},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4639662504196167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44178518652915955},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4099668860435486},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.2934887409210205},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.27859726548194885},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20652076601982117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8487282395362854},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6769121289253235},{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.6529179811477661},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6224391460418701},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.507532000541687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48270726203918457},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.47518932819366455},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.4683491587638855},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4639662504196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44178518652915955},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4099668860435486},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2934887409210205},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27859726548194885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20652076601982117}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006000","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006000","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W56544557","https://openalex.org/W88685657","https://openalex.org/W1511814458","https://openalex.org/W1516903196","https://openalex.org/W1568804695","https://openalex.org/W1574535057","https://openalex.org/W1678356000","https://openalex.org/W1971286892","https://openalex.org/W1972484642","https://openalex.org/W1973079148","https://openalex.org/W1979120705","https://openalex.org/W1992282993","https://openalex.org/W2004957971","https://openalex.org/W2031533839","https://openalex.org/W2070493638","https://openalex.org/W2090068900","https://openalex.org/W2107469355","https://openalex.org/W2108263314","https://openalex.org/W2110287632","https://openalex.org/W2114109296","https://openalex.org/W2130758224","https://openalex.org/W2156030242","https://openalex.org/W2159486442","https://openalex.org/W2168231600","https://openalex.org/W2177209050","https://openalex.org/W2183863591","https://openalex.org/W2275295769","https://openalex.org/W2295598076","https://openalex.org/W2520615899","https://openalex.org/W2539133644","https://openalex.org/W2579247884","https://openalex.org/W2605592668","https://openalex.org/W2768348081","https://openalex.org/W2772852940","https://openalex.org/W2782492233","https://openalex.org/W2886223758","https://openalex.org/W2891866930","https://openalex.org/W3024083111","https://openalex.org/W3102476541","https://openalex.org/W4251546625","https://openalex.org/W6630671948","https://openalex.org/W6630841318","https://openalex.org/W6634281411","https://openalex.org/W6634418898","https://openalex.org/W6637404493","https://openalex.org/W6676133050","https://openalex.org/W6676246221","https://openalex.org/W6676249281","https://openalex.org/W6684859321","https://openalex.org/W6686366851","https://openalex.org/W6694664753","https://openalex.org/W6726946094","https://openalex.org/W6728887892","https://openalex.org/W6745609711","https://openalex.org/W6753995509","https://openalex.org/W6755135904"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3137904399","https://openalex.org/W4308419224","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W4310224730","https://openalex.org/W3094138326"],"abstract_inverted_index":{"Federated":[0],"machine":[1],"learning":[2,88],"systems":[3],"have":[4],"been":[5],"widely":[6],"used":[7],"to":[8,105,128,184,196,201,268],"facilitate":[9],"the":[10,15,20,71,106,116,133,164,173,177,185,203,208,219,228,239,250,270,284,295,298,303,307],"joint":[11],"data":[12,69,124,221],"analytics":[13],"across":[14],"distributed":[16],"datasets":[17],"owned":[18],"by":[19,172],"different":[21],"parties":[22,93,241],"that":[23,223],"do":[24],"not":[25,126],"trust":[26],"each":[27],"others.":[28],"In":[29,118,190],"this":[30,191],"paper,":[31],"we":[32,136,193],"proposed":[33,195,271],"a":[34,44,59,84,142,213,259],"novel":[35],"Gradient":[36,61],"Boosting":[37,62],"Machines":[38],"(GBM)":[39],"framework":[40],"SecureGBM":[41,224,272,288],"built-up":[42],"with":[43,130,158,275],"multi-party":[45],"computation":[46,231],"model":[47,64],"based":[48,90,180],"on":[49,83,91,181,258],"semi-homomorphic":[50],"encryption,":[51],"where":[52],"every":[53],"involved":[54],"party":[55,96,114],"can":[56,225],"jointly":[57],"obtain":[58],"shared":[60],"machines":[63],"while":[65,111,206,242,287],"protecting":[66],"their":[67],"own":[68,97],"from":[70],"potential":[72],"privacy":[73],"leakage":[74],"and":[75,122,156,230,235,291],"inferential":[76],"identification.":[77],"More":[78,265],"specific,":[79,266],"our":[80],"work":[81],"focused":[82],"specific":[85],"\u201cdualparty\u201d":[86],"secure":[87,227],"scenario":[89],"two":[92],"\u2014":[94,139,141],"both":[95,240],"an":[98],"unique":[99],"view":[100],"(i.e.,":[101,302],"attributes":[102],"or":[103],"features)":[104],"sample":[107],"group":[108],"of":[109,146,166,176,188,232,253,262,297],"samples":[110],"only":[112,243],"one":[113],"owns":[115],"labels.":[117],"such":[119,167],"scenario,":[120],"feature":[121],"label":[123],"are":[125],"allowed":[127],"share":[129],"others.To":[131],"achieve":[132],"above":[134],"goal,":[135],"firstly":[137],"extent":[138],"LightGBM":[140,233],"well":[143,226],"known":[144],"implementation":[145],"tree-based":[147],"GBM":[148],"through":[149],"covering":[150],"its":[151],"key":[152],"operations":[153],"for":[154,238,255],"training":[155,210,234,285,299,305],"inference":[157,236],"SEAL":[159],"homomorphic":[160],"encryption":[161],"schemes.":[162],"However,":[163],"performance":[165],"re-implementation":[168],"is":[169],"significantly":[170],"bottle-necked":[171],"explosive":[174],"inflation":[175],"communication":[178,204,229],"payloads,":[179],"ciphertexts":[182],"subject":[183],"increasing":[186],"length":[187],"plaintexts.":[189],"way,":[192],"then":[194],"use":[197],"stochastic":[198],"approximation":[199],"techniques":[200],"reduced":[202],"payloads":[205],"accelerating":[207],"overall":[209],"procedure":[211],"in":[212,283],"statistical":[214],"manner.":[215],"Our":[216],"experiments":[217],"using":[218,249],"real-world":[220],"showed":[222],"procedures":[237],"losing":[244],"less":[245],"than":[246],"3%":[247],"AUC,":[248],"same":[251],"number":[252],"iterations":[254],"gradient":[256],"boosting,":[257],"wide":[260],"range":[261],"benchmark":[263],"datasets.":[264],"compared":[267],"LightGBM,":[269],"would":[273],"slowdown":[274,309],"$3\\mathrm{x}":[276],"\\sim":[277],"64\\mathrm{x}$":[278],"time":[279],"consumption":[280],"per":[281],"iteration":[282],"procedure,":[286],"becomes":[289],"more":[290,292],"efficient":[293],"when":[294],"scale":[296],"dataset":[300],"increases":[301],"larger":[304],"set,":[306],"lower":[308],"ratio).":[310]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}