{"id":"https://openalex.org/W4405562223","doi":"https://doi.org/10.1109/eurosp68448.2026.00045","title":"GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning","display_name":"GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning","publication_year":2026,"publication_date":"2026-07-01","ids":{"openalex":"https://openalex.org/W4405562223","doi":"https://doi.org/10.1109/eurosp68448.2026.00045"},"language":"en","primary_location":{"id":"doi:10.1109/eurosp68448.2026.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurosp68448.2026.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 11th European Symposium on Security and Privacy (EuroS&amp;P)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.12640","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100422048","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0002-4221-1311"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["The University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028240524","display_name":"Ka-Ho Chow","orcid":"https://orcid.org/0000-0001-5917-2577"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ka-Ho Chow","raw_affiliation_strings":["The University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00437202,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"592","last_page":"607"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9962999820709229,"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":0.9962999820709229,"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":0.9517999887466431,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9506999850273132,"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/leakage","display_name":"Leakage (economics)","score":0.7348573207855225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4622281491756439},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.11328455805778503}],"concepts":[{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.7348573207855225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4622281491756439},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.11328455805778503},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/eurosp68448.2026.00045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eurosp68448.2026.00045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 11th European Symposium on Security and Privacy (EuroS&amp;P)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2412.12640","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.12640","pdf_url":"https://arxiv.org/pdf/2412.12640","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"","raw_type":"text"},{"id":"pmh:oai:arXiv.org:2412.12640","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2412.12640","pdf_url":"https://arxiv.org/pdf/2412.12640","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2412.12640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2412.12640","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2412.12640","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.12640","pdf_url":"https://arxiv.org/pdf/2412.12640","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405562223.pdf","grobid_xml":"https://content.openalex.org/works/W4405562223.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0],"increasing":[1],"demand":[2],"for":[3,72,179,204,222,269],"data":[4,11,210],"privacy,":[5],"alongside":[6],"the":[7,17,42,84,96,102,136,148,166,170,176,236,239,259,265],"benefits":[8],"of":[9,19,100,112,126,140,182,238,245],"aggregating":[10],"from":[12,58,165],"networked":[13],"devices,":[14],"has":[15,66],"catalyzed":[16],"emergence":[18],"federated":[20],"learning":[21],"(FL).":[22],"In":[23,228],"FL,":[24],"clients":[25],"jointly":[26],"train":[27],"a":[28,69,109,122,152,155,162,194,202,242],"global":[29],"model":[30,248],"by":[31],"sharing":[32,120],"gradients":[33,86,127],"computed":[34],"over":[35],"private":[36,183,196],"data.":[37],"While":[38],"this":[39,61,106],"paradigm":[40],"eliminates":[41],"need":[43],"to":[44,54,89,130,147,169,230],"exchange":[45],"raw":[46],"data,":[47],"inference":[48,92,181],"attacks":[49,93],"can":[50,128],"still":[51],"be":[52],"launched":[53],"extract":[55],"sensitive":[56,191],"information":[57,178,192],"gradients.":[59,149],"To":[60],"end,":[62],"partial":[63,145,226],"gradient":[64,163],"encryption":[65],"emerged":[67],"as":[68,81,201,209],"promising":[70],"design":[71,107],"balancing":[73],"privacy":[74,132,270],"and":[75,174,212,247,252],"efficiency":[76],"in":[77,154],"practical":[78],"FL":[79,223],"systems,":[80],"encrypting":[82,101,263],"only":[83,189,264],"classification-head":[85],"is":[87,135],"believed":[88],"prevent":[90],"known":[91],"while":[94],"avoiding":[95],"high":[97],"computational":[98],"cost":[99],"entire":[103],"model.":[104],"However,":[105],"provides":[108],"false":[110],"sense":[111],"privacy.":[113],"By":[114],"proposing":[115],"GDBR,":[116],"we":[117],"show":[118],"that":[119,262],"even":[121],"single":[123],"unencrypted":[124,167],"layer":[125,168,267],"lead":[129],"serious":[131],"leakage.":[133],"GDBR":[134,215],"first":[137],"attack":[138],"capable":[139],"high-fidelity":[141],"label":[142],"recovery":[143],"with":[144],"access":[146],"It":[150],"exploits":[151],"vulnerability":[153],"commonly":[156],"used":[157],"neural":[158],"building":[159],"block,":[160],"constructs":[161],"bridge":[164],"final":[171],"output":[172,266],"layer,":[173],"approximates":[175],"logits":[177],"accurate":[180],"labels.":[184],"These":[185],"inferred":[186],"labels":[187],"not":[188],"reveal":[190],"about":[193],"client's":[195],"dataset":[197],"but":[198],"also":[199],"serve":[200],"prerequisite":[203],"many":[205],"downstream":[206],"attacks,":[207],"such":[208],"reconstruction":[211],"membership":[213],"inference.":[214],"brings":[216],"these":[217],"threats":[218],"squarely":[219],"into":[220],"scope":[221],"systems":[224],"employing":[225],"encryption.":[227],"addition":[229],"theoretical":[231],"analysis,":[232],"extensive":[233],"experiments":[234],"demonstrate":[235],"severity":[237],"problem":[240],"across":[241],"wide":[243],"variety":[244],"datasets":[246],"architectures,":[249],"including":[250],"convolutional":[251],"transformer-based":[253],"networks.":[254],"Overall,":[255],"our":[256],"findings":[257],"challenge":[258],"widespread":[260],"assumption":[261],"suffices":[268],"protection.":[271]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
