{"id":"https://openalex.org/W4387846538","doi":"https://doi.org/10.1145/3583780.3615203","title":"Differential Privacy in HyperNetworks for Personalized Federated Learning","display_name":"Differential Privacy in HyperNetworks for Personalized Federated Learning","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387846538","doi":"https://doi.org/10.1145/3583780.3615203"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615203","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615203","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615203","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615203","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093105957","display_name":"Vaisnavi Nemala","orcid":"https://orcid.org/0009-0007-6622-177X"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vaisnavi Nemala","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103165051","display_name":"Phung Lai","orcid":"https://orcid.org/0009-0007-8019-2303"},"institutions":[{"id":"https://openalex.org/I113508548","display_name":"Albany State University","ror":"https://ror.org/01vme4277","country_code":"US","type":"education","lineage":["https://openalex.org/I113508548"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Phung Lai","raw_affiliation_strings":["University at Albany - State University of New York, Albany, NY, USA"],"affiliations":[{"raw_affiliation_string":"University at Albany - State University of New York, Albany, NY, USA","institution_ids":["https://openalex.org/I113508548","https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060481572","display_name":"NhatHai Phan","orcid":"https://orcid.org/0000-0002-1032-8275"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nhathai Phan","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5093105957"],"corresponding_institution_ids":["https://openalex.org/I118118575"],"apc_list":null,"apc_paid":null,"fwci":0.5219,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72356288,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4224","last_page":"4228"},"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.927299976348877,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.9042364358901978},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8166046142578125},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7184518575668335},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7043287754058838},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.43577277660369873},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4318382740020752},{"id":"https://openalex.org/keywords/privacy-protection","display_name":"Privacy protection","score":0.42751646041870117},{"id":"https://openalex.org/keywords/information-sensitivity","display_name":"Information sensitivity","score":0.4273257851600647},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.321353942155838},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3167823553085327},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.31298795342445374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26729029417037964}],"concepts":[{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.9042364358901978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8166046142578125},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7184518575668335},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7043287754058838},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.43577277660369873},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4318382740020752},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.42751646041870117},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.4273257851600647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.321353942155838},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3167823553085327},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31298795342445374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26729029417037964},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615203","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615203","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615203","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3615203","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615203","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615203","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G2306483230","display_name":null,"funder_award_id":"2041096","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387846538.pdf","grobid_xml":"https://content.openalex.org/works/W4387846538.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1834627138","https://openalex.org/W1873763122","https://openalex.org/W2077217970","https://openalex.org/W2088491382","https://openalex.org/W2112796928","https://openalex.org/W2473418344","https://openalex.org/W3034163621","https://openalex.org/W3035668299","https://openalex.org/W3038022836","https://openalex.org/W3046764764","https://openalex.org/W3080934299","https://openalex.org/W4285762978","https://openalex.org/W4285876308","https://openalex.org/W4318185106","https://openalex.org/W6759238902"],"related_works":["https://openalex.org/W4286971788","https://openalex.org/W3199340467","https://openalex.org/W3157608626","https://openalex.org/W3132132958","https://openalex.org/W4321612632","https://openalex.org/W4322580403","https://openalex.org/W4399147128","https://openalex.org/W3193217249","https://openalex.org/W4280591108","https://openalex.org/W4388855833"],"abstract_inverted_index":{"Federated":[0],"learning":[1,8],"(FL)":[2],"is":[3,23],"a":[4,12,46,67,107],"framework":[5],"for":[6,33,62],"collaborative":[7],"among":[9],"users":[10],"through":[11],"coordinating":[13],"server.":[14],"A":[15],"recent":[16],"HyperNetwork-based":[17],"personalized":[18,30],"FL":[19],"framework,":[20],"called":[21,76],"HyperNetFL,":[22],"used":[24],"to":[25,49],"generate":[26],"local":[27],"models":[28],"using":[29,94],"descriptors":[31],"optimized":[32],"each":[34],"user":[35],"independently.":[36],"However,":[37],"HyperNetFL":[38,68],"introduces":[39,45],"unknown":[40],"privacy":[41,60,82,104],"risks.":[42],"This":[43],"paper":[44],"novel":[47],"approach":[48],"preserve":[50],"user-level":[51],"differential":[52],"privacy,":[53],"dubbed":[54],"User-level":[55],"DP,":[56],"by":[57,87],"providing":[58],"formal":[59],"protection":[61,105],"data":[63],"owners":[64],"in":[65,110],"training":[66],"model.":[69],"To":[70],"achieve":[71],"that,":[72],"our":[73,99],"proposed":[74,100],"algorithm,":[75],"UDP-Alg,":[77],"optimizes":[78],"the":[79],"trade-off":[80],"between":[81],"loss":[83],"and":[84],"model":[85],"utility":[86],"tightening":[88],"sensitivity":[89],"bounds.":[90],"An":[91],"intensive":[92],"evaluation":[93],"benchmark":[95],"datasets":[96],"shows":[97],"that":[98],"UDP-Alg":[101],"significantly":[102],"improves":[103],"at":[106],"modest":[108],"cost":[109],"utility.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}