{"id":"https://openalex.org/W3048641832","doi":"https://doi.org/10.1145/3340531.3411860","title":"Towards Plausible Differentially Private ADMM Based Distributed Machine Learning","display_name":"Towards Plausible Differentially Private ADMM Based Distributed Machine Learning","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3048641832","doi":"https://doi.org/10.1145/3340531.3411860","mag":"3048641832"},"language":"en","primary_location":{"id":"doi:10.1145/3340531.3411860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.04500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jiahao Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiahao Ding","raw_affiliation_strings":["University of Houston, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jingyi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I71838634","display_name":"San Francisco State University","ror":"https://ror.org/05ykr0121","country_code":"US","type":"education","lineage":["https://openalex.org/I71838634"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingyi Wang","raw_affiliation_strings":["San Francisco State University, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"San Francisco State University, San Francisco, CA, USA","institution_ids":["https://openalex.org/I71838634"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Guannan Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guannan Liang","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinbo Bi","orcid":null},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinbo Bi","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":null,"display_name":"Miao Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Miao Pan","raw_affiliation_strings":["University of Houston, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"285","last_page":"294"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9958999752998352,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.9150999784469604},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6093000173568726},{"id":"https://openalex.org/keywords/premise","display_name":"Premise","score":0.5989000201225281},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4830000102519989},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.439300000667572},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41780000925064087},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.35100001096725464},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.33730000257492065},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3124000132083893}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.9150999784469604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6779000163078308},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C2778023277","wikidata":"https://www.wikidata.org/wiki/Q321703","display_name":"Premise","level":2,"score":0.5989000201225281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5289000272750854},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44130000472068787},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41780000925064087},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4043000042438507},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39419999718666077},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3124000132083893},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C2986577269","wikidata":"https://www.wikidata.org/wiki/Q11306265","display_name":"Random noise","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C130120984","wikidata":"https://www.wikidata.org/wiki/Q2835898","display_name":"Distributed algorithm","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C41550386","wikidata":"https://www.wikidata.org/wiki/Q529909","display_name":"Multi-agent system","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3340531.3411860","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3340531.3411860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.04500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.04500","pdf_url":"https://arxiv.org/pdf/2008.04500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.04500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.04500","pdf_url":"https://arxiv.org/pdf/2008.04500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1982545958","https://openalex.org/W2092620240","https://openalex.org/W2124612670","https://openalex.org/W2151565294","https://openalex.org/W2509467699","https://openalex.org/W2895296474","https://openalex.org/W2918899415","https://openalex.org/W2927692314","https://openalex.org/W2963506795","https://openalex.org/W2998690112","https://openalex.org/W3101329503","https://openalex.org/W6600020652","https://openalex.org/W6600192831"],"related_works":[],"abstract_inverted_index":{"The":[0,87,185],"Alternating":[1],"Direction":[2],"Method":[3],"of":[4,21,58,69,190,261,266],"Multipliers":[5],"(ADMM)":[6],"and":[7,29,117,140,160,200,236,269],"its":[8,133,178],"distributed":[9],"version":[10,164],"have":[11,94],"been":[12],"widely":[13],"used":[14],"in":[15,77,85,137,196,264],"machine":[16],"learning.":[17],"In":[18,98,119],"the":[19,56,64,67,143,151,157,181,188,191,197,202,211,217,227,232,249,253,259,262],"iterations":[20],"ADMM,":[22],"model":[23,30,88,158,267],"updates":[24],"using":[25],"local":[26,71,134],"private":[27,47,135],"data":[28,136],"exchanges":[31],"among":[32],"agents":[33],"impose":[34],"critical":[35],"privacy":[36,60,229,251],"concerns.":[37],"Despite":[38],"some":[39],"pioneering":[40],"works":[41],"to":[42,149,171,213,225,258],"relieve":[43],"such":[44],"concerns,":[45],"differentially":[46,111],"ADMM":[48,79,92,113],"still":[49],"confronts":[50],"many":[51],"research":[52],"challenges.":[53],"For":[54],"example,":[55],"guarantee":[57],"differential":[59],"(DP)":[61],"relies":[62],"on":[63,243],"premise":[65],"that":[66,129,195,247],"optimality":[68],"each":[70,78,121],"problem":[72,128],"can":[73],"be":[74,220],"perfectly":[75],"attained":[76],"iteration,":[80,139,199],"which":[81],"may":[82,93],"never":[83],"happen":[84],"practice.":[86],"trained":[89],"by":[90,105],"DP":[91,152,234],"low":[95],"prediction":[96],"accuracy.":[97],"this":[99],"paper,":[100],"we":[101,223],"address":[102],"these":[103],"concerns":[104],"proposing":[106],"a":[107,125,207,238],"novel":[108],"(Improved)":[109],"Plausible":[110],"Private":[112],"algorithm,":[114],"called":[115],"PP-ADMM":[116],"IPP-ADMM.":[118],"PP-ADMM,":[120],"agent":[122,175,186],"approximately":[123],"solves":[124],"perturbed":[126,183],"optimization":[127],"is":[130,204],"formulated":[131],"from":[132,194],"an":[138,162,174],"then":[141],"perturbs":[142],"approximate":[144],"solution":[145,193,212,218],"with":[146,180],"Gaussian":[147],"noise":[148],"provide":[150,237],"guarantee.":[153],"To":[154],"further":[155],"improve":[156],"accuracy":[159,268],"convergence,":[161],"improved":[163],"IPP-ADMM":[165],"adopts":[166],"sparse":[167],"vector":[168],"technique":[169],"(SVT)":[170],"determine":[172],"if":[173,201],"should":[176],"update":[177],"neighbors":[179],"current":[182,192],"solution.":[184],"calculates":[187],"difference":[189,203],"last":[198],"larger":[205],"than":[206],"threshold,":[208],"it":[209],"passes":[210],"neighbors;":[214],"or":[215],"otherwise":[216],"will":[219],"discarded.":[221],"Moreover,":[222],"propose":[224],"track":[226],"total":[228],"loss":[230],"under":[231,248],"zero-concentrated":[233],"(zCDP)":[235],"generalization":[239],"performance":[240],"analysis.":[241],"Experiments":[242],"real-world":[244],"datasets":[245],"demonstrate":[246],"same":[250],"guarantee,":[252],"proposed":[254],"algorithms":[255],"are":[256],"superior":[257],"state":[260],"art":[263],"terms":[265],"convergence":[270],"rate.":[271]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2020-08-18T00:00:00"}