{"id":"https://openalex.org/W4312646383","doi":"https://doi.org/10.14778/3565816.3565823","title":"OpBoost","display_name":"OpBoost","publication_year":2022,"publication_date":"2022-10-01","ids":{"openalex":"https://openalex.org/W4312646383","doi":"https://doi.org/10.14778/3565816.3565823"},"language":"en","primary_location":{"id":"doi:10.14778/3565816.3565823","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3565816.3565823","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","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/A5100328757","display_name":"Xiaochen Li","orcid":"https://orcid.org/0000-0002-3722-4783"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaochen Li","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005443916","display_name":"Yuke Hu","orcid":"https://orcid.org/0000-0001-5780-6898"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuke Hu","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101802649","display_name":"Weiran Liu","orcid":"https://orcid.org/0000-0002-1466-7418"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiran Liu","raw_affiliation_strings":["Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080912865","display_name":"Hanwen Feng","orcid":"https://orcid.org/0000-0002-7069-5165"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanwen Feng","raw_affiliation_strings":["Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104097999","display_name":"Li Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Peng","raw_affiliation_strings":["Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725148","display_name":"Yuan Hong","orcid":"https://orcid.org/0000-0003-4095-4506"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan Hong","raw_affiliation_strings":["University of Connecticut"],"affiliations":[{"raw_affiliation_string":"University of Connecticut","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000596496","display_name":"Kui Ren","orcid":"https://orcid.org/0000-0003-3441-6277"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kui Ren","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043524348","display_name":"Zhan Qin","orcid":"https://orcid.org/0000-0001-7872-6969"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhan Qin","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100328757"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":3.6095,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.93805642,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"16","issue":"2","first_page":"202","last_page":"215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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":0.9783999919891357,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9474999904632568,"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":0.8527432680130005},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7588849067687988},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.6664906144142151},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.54047030210495},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5041002035140991},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.49875473976135254},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.484507292509079},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4602988362312317},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.4504103660583496},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44265297055244446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40563613176345825},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3720717430114746}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8527432680130005},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7588849067687988},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.6664906144142151},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.54047030210495},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5041002035140991},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.49875473976135254},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.484507292509079},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4602988362312317},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4504103660583496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44265297055244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40563613176345825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3720717430114746},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3565816.3565823","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3565816.3565823","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1658920975","https://openalex.org/W1707848225","https://openalex.org/W1895952394","https://openalex.org/W1981029888","https://openalex.org/W1999670653","https://openalex.org/W2001347093","https://openalex.org/W2042946599","https://openalex.org/W2074388704","https://openalex.org/W2082894754","https://openalex.org/W2092071682","https://openalex.org/W2109426455","https://openalex.org/W2115584760","https://openalex.org/W2154496743","https://openalex.org/W2245160765","https://openalex.org/W2295598076","https://openalex.org/W2532292902","https://openalex.org/W2539106208","https://openalex.org/W2726905439","https://openalex.org/W2760861505","https://openalex.org/W2887818006","https://openalex.org/W2889227180","https://openalex.org/W2912213068","https://openalex.org/W2963629772","https://openalex.org/W2963697313","https://openalex.org/W2963881987","https://openalex.org/W2973232880","https://openalex.org/W2981759742","https://openalex.org/W2997721570","https://openalex.org/W2998640192","https://openalex.org/W3031432927","https://openalex.org/W3080876524","https://openalex.org/W3094086913","https://openalex.org/W3096074096","https://openalex.org/W3102994281","https://openalex.org/W3106047101","https://openalex.org/W3111157675","https://openalex.org/W3164712068","https://openalex.org/W3175418606","https://openalex.org/W3211363117","https://openalex.org/W4205228770"],"related_works":["https://openalex.org/W2604501336","https://openalex.org/W2734500670","https://openalex.org/W2558166297","https://openalex.org/W2315671126","https://openalex.org/W798507144","https://openalex.org/W2125652721","https://openalex.org/W2964481303","https://openalex.org/W1540371141","https://openalex.org/W2571704763","https://openalex.org/W4231274751"],"abstract_inverted_index":{"Vertical":[0],"Federated":[1],"Learning":[2],"(FL)":[3],"is":[4,221],"a":[5,22,129,138,180,202],"new":[6],"paradigm":[7],"that":[8,34,199],"enables":[9],"users":[10],"with":[11,187,212],"non-overlapping":[12],"attributes":[13],"of":[14,102,113,140,172,185,193,208],"the":[15,27,44,48,56,62,84,93,99,103,111,114,150,157,168,173,183,191],"same":[16],"data":[17],"samples":[18],"to":[19,39,80,98,109,148,165],"jointly":[20],"train":[21],"model":[23],"without":[24],"directly":[25],"sharing":[26],"raw":[28],"data.":[29,152],"Nevertheless,":[30],"recent":[31],"works":[32],"show":[33,198],"it's":[35],"still":[36],"not":[37],"sufficient":[38],"prevent":[40],"privacy":[41,122,184],"leakage":[42],"from":[43],"training":[45,151],"process":[46],"or":[47],"trained":[49,104,209],"model.":[50,105],"This":[51,106],"paper":[52,107],"focuses":[53],"on":[54,69,87,205,216],"studying":[55],"privacy-preserving":[57],"tree":[58,117],"boosting":[59,118],"algorithms":[60,119,136,178],"under":[61,123],"vertical":[63,124],"FL.":[64,125],"The":[65,176],"existing":[66,213],"solutions":[67],"based":[68,86],"cryptography":[70],"involve":[71],"heavy":[72],"computation":[73],"and":[74,77,160,170,190],"communication":[75],"overhead":[76],"are":[78,146],"vulnerable":[79],"inference":[81],"attacks.":[82],"Although":[83],"solution":[85],"Local":[88],"Differential":[89],"Privacy":[90],"(LDP)":[91],"addresses":[92],"above":[94],"problems,":[95],"it":[96],"leads":[97],"low":[100],"accuracy":[101,112,169,207],"explores":[108],"improve":[110,167],"widely":[115],"deployed":[116],"satisfying":[120,137],"differential":[121],"Specifically,":[126],"we":[127,155],"introduce":[128],"framework":[130],"called":[131,142],"OpBoost.":[132],"Three":[133],"order-preserving":[134],"desensitization":[135],"variant":[139],"LDP":[141,144,214],"distance-based":[143],"(dLDP)":[145],"designed":[147],"desensitize":[149],"In":[153],"particular,":[154],"optimize":[156],"dLDP":[158],"definition":[159],"study":[161],"efficient":[162],"sampling":[163],"distributions":[164],"further":[166],"efficiency":[171],"proposed":[174,177],"algorithms.":[175],"provide":[179],"trade-off":[181],"between":[182],"pairs":[186],"large":[188],"distance":[189],"utility":[192],"desensitized":[194],"values.":[195],"Comprehensive":[196],"evaluations":[197],"OpBoost":[200],"has":[201],"better":[203],"performance":[204],"prediction":[206],"models":[210],"compared":[211],"approaches":[215],"reasonable":[217],"settings.":[218],"Our":[219],"code":[220],"open":[222],"source.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-06T07:47:59.780226","created_date":"2023-01-05T00:00:00"}