{"id":"https://openalex.org/W6929287846","doi":"https://doi.org/10.48550/arxiv.2502.08151","title":"Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy","display_name":"Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy","publication_year":2025,"publication_date":"2025-02-12","ids":{"openalex":"https://openalex.org/W6929287846","doi":"https://doi.org/10.48550/arxiv.2502.08151"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2502.08151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.08151","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2502.08151","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"You, Zhichao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"You, Zhichao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Dong, Xuewen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Xuewen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Shujun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shujun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Ximeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ximeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Ma, Siqi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Siqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shen, Yulong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yulong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11190","display_name":"3D Printing in Biomedical Research","score":0.2833999991416931,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11190","display_name":"3D Printing in Biomedical Research","score":0.2833999991416931,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.10260000079870224,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10336","display_name":"Cancer Cells and Metastasis","score":0.04960000142455101,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.7003999948501587},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.6367999911308289},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.6205999851226807},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4805000126361847},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.45719999074935913},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44940000772476196},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.3977000117301941}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.7003999948501587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686999797821045},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.6367999911308289},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.6205999851226807},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44940000772476196},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42649999260902405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3179999887943268},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31679999828338623},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C40305131","wikidata":"https://www.wikidata.org/wiki/Q2616305","display_name":"Obfuscation","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.2628999948501587},{"id":"https://openalex.org/C2779898584","wikidata":"https://www.wikidata.org/wiki/Q7820109","display_name":"Reconstruction algorithm","level":3,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2502.08151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.08151","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2502.08151","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.08151","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5288681983947754,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reconstruction":[0],"attacks":[1,40,60,122,228],"against":[2,25,96,156,225],"federated":[3],"learning":[4],"(FL)":[5],"aim":[6],"to":[7,67,77,103,108,152,167,175,223],"reconstruct":[8,104],"users'":[9,12],"samples":[10,107,202],"through":[11],"uploaded":[13],"gradients.":[14],"Local":[15],"differential":[16],"privacy":[17],"(LDP)":[18],"is":[19,114,133,150,194],"regarded":[20],"as":[21],"an":[22,143],"effective":[23],"defense":[24],"various":[26],"attacks,":[27],"including":[28],"sample":[29,54,63,93,138,148,160,226],"reconstruction":[30,94,121,227],"in":[31,43,83,120,125,203],"FL,":[32],"where":[33],"gradients":[34,51,66,155,166],"are":[35,41],"clipped":[36,48],"and":[37,49,72,123,136,171,206],"perturbed.":[38],"Existing":[39],"ineffective":[42],"FL":[44,84,98,111,205,219],"with":[45,85,99,112],"LDP":[46,113],"since":[47],"perturbed":[50],"obliterate":[52],"most":[53],"information":[55,64],"for":[56],"reconstruction.":[57],"Besides,":[58],"existing":[59],"embed":[61],"additional":[62],"into":[65],"improve":[68],"the":[69,127,130,177,182,185,191,195,211],"attack":[70,95,132,193,197],"effect":[71],"cause":[73],"gradient":[74,81,118,134,141],"expansion,":[75],"leading":[76],"a":[78,92],"more":[79],"severe":[80],"clipping":[82],"LDP.":[86,157],"In":[87],"this":[88],"paper,":[89],"we":[90,162],"propose":[91],"LDP-based":[97,204,218],"any":[100],"target":[101,212],"models":[102],"victims'":[105,200],"sensitive":[106],"illustrate":[109],"that":[110,190,198,217],"not":[115],"flawless.":[116],"Considering":[117],"expansion":[119],"noise":[124,169],"LDP,":[126],"core":[128],"of":[129,184],"proposed":[131,186,192],"compression":[135],"reconstructed":[137,159],"denoising.":[139],"For":[140,158],"compression,":[142],"inference":[144],"structure":[145],"based":[146],"on":[147,210],"characteristics":[149],"presented":[151],"reduce":[153],"redundant":[154],"denoising,":[161],"artificially":[163],"introduce":[164],"zero":[165],"observe":[168],"distribution":[170],"scale":[172],"confidence":[173],"interval":[174],"filter":[176],"noise.":[178],"Theoretical":[179],"proof":[180],"guarantees":[181],"effectiveness":[183],"attack.":[187],"Evaluations":[188],"show":[189],"only":[196],"reconstructs":[199],"training":[201],"has":[207],"little":[208],"impact":[209],"model's":[213],"accuracy.":[214],"We":[215],"conclude":[216],"needs":[220],"further":[221],"improvements":[222],"defend":[224],"effectively.":[229]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
