{"id":"https://openalex.org/W4224017984","doi":"https://doi.org/10.1145/3523150.3523171","title":"Privacy-Preserving Vertical Federated Logistic Regression without Trusted Third-Party Coordinator","display_name":"Privacy-Preserving Vertical Federated Logistic Regression without Trusted Third-Party Coordinator","publication_year":2022,"publication_date":"2022-01-15","ids":{"openalex":"https://openalex.org/W4224017984","doi":"https://doi.org/10.1145/3523150.3523171"},"language":"en","primary_location":{"id":"doi:10.1145/3523150.3523171","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","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/A5037832021","display_name":"Huizhong Sun","orcid":"https://orcid.org/0000-0002-1232-2053"},"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":true,"raw_author_name":"Huizhong Sun","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101729389","display_name":"Zhenya Wang","orcid":null},"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":"Zhenya Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110003966","display_name":"Yuejia Huang","orcid":null},"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":"Yuejia Huang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051671090","display_name":"Junda Ye","orcid":"https://orcid.org/0000-0002-2900-4908"},"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":"Junda Ye","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5037832021"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.0613,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80053557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"132","last_page":"138"},"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":0.9894999861717224,"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":0.9697999954223633,"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":0.8245567083358765},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7798051238059998},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.5774306654930115},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.5472351312637329},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5430036187171936},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5266723036766052},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.49991726875305176},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4909668266773224},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.42241576313972473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28531360626220703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24683624505996704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8245567083358765},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7798051238059998},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.5774306654930115},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.5472351312637329},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5430036187171936},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5266723036766052},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.49991726875305176},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4909668266773224},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.42241576313972473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28531360626220703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24683624505996704},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3523150.3523171","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3523150.3523171","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 The 6th International Conference on Machine Learning and Soft Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W44936433","https://openalex.org/W1557833142","https://openalex.org/W2027595342","https://openalex.org/W2051267297","https://openalex.org/W2132172731","https://openalex.org/W2138865266","https://openalex.org/W2473418344","https://openalex.org/W2535199873","https://openalex.org/W2535690855","https://openalex.org/W2606882085","https://openalex.org/W2701059868","https://openalex.org/W2767079719","https://openalex.org/W2801995427","https://openalex.org/W2912213068","https://openalex.org/W2921450191","https://openalex.org/W2927692314","https://openalex.org/W3102360395","https://openalex.org/W4299689471","https://openalex.org/W6657138077"],"related_works":["https://openalex.org/W3022534164","https://openalex.org/W3046095319","https://openalex.org/W3197497514","https://openalex.org/W1591172238","https://openalex.org/W2111194702","https://openalex.org/W2972172135","https://openalex.org/W1787552957","https://openalex.org/W315296216","https://openalex.org/W4300474583","https://openalex.org/W3147973582"],"abstract_inverted_index":{"Federated":[0],"learning":[1,6,35],"is":[2,37,78],"a":[3,15,25,63,75,88],"new":[4],"distributed":[5],"paradigm,":[7],"which":[8],"allows":[9],"multiple":[10],"parties":[11],"to":[12,81],"cooperatively":[13],"train":[14],"centralized":[16],"model":[17,66,86,114],"without":[18,71],"sharing":[19],"their":[20],"data.":[21],"In":[22],"this":[23,40],"paper,":[24],"privacy-preserving":[26],"logistic":[27],"regression":[28],"(LR)":[29],"training":[30,67],"algorithm":[31,90,102],"for":[32,69],"vertical":[33],"federated":[34],"(VFL)":[36],"proposed.":[38],"First,":[39],"paper":[41],"analyzes":[42],"the":[43,49,56,72,83,101,106,113],"related":[44],"works":[45],"and":[46,59,91,108],"point":[47],"out":[48],"privacy":[50,84,93],"leakage":[51],"risks.":[52],"Then,":[53],"based":[54],"on":[55],"mini-batch":[57],"SGD":[58],"parameter":[60],"encryption":[61],"method,":[62],"secure":[64],"VFL":[65],"scheme":[68],"LR":[70],"assistance":[73],"of":[74,85],"trusted":[76],"third-party":[77],"designed.":[79],"Next,":[80],"protect":[82],"parameters,":[87],"differentially-private":[89],"comprehensive":[92],"analysis":[94],"are":[95],"provided.":[96],"Finally,":[97],"experiments":[98],"show":[99],"that":[100],"not":[103],"only":[104],"guarantees":[105],"security":[107],"privacy,":[109],"but":[110],"also":[111],"ensures":[112],"utility.":[115]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
