{"id":"https://openalex.org/W4414199214","doi":"https://doi.org/10.1109/lanman66415.2025.11154574","title":"Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality","display_name":"Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality","publication_year":2025,"publication_date":"2025-07-07","ids":{"openalex":"https://openalex.org/W4414199214","doi":"https://doi.org/10.1109/lanman66415.2025.11154574"},"language":"en","primary_location":{"id":"doi:10.1109/lanman66415.2025.11154574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lanman66415.2025.11154574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31st International Symposium on Local and Metropolitan Area Networks (LANMAN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2603.02969","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089061334","display_name":"Yenan Wang","orcid":"https://orcid.org/0000-0003-0450-8680"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Yenan Wang","raw_affiliation_strings":["Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021780258","display_name":"Carla Fabiana Chiasserini","orcid":"https://orcid.org/0000-0003-1410-660X"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Carla Fabiana Chiasserini","raw_affiliation_strings":["Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043628304","display_name":"Elad M. Schiller","orcid":"https://orcid.org/0000-0003-3258-3696"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Elad Michael Schiller","raw_affiliation_strings":["Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5089061334"],"corresponding_institution_ids":["https://openalex.org/I66862912"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1348135,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.972599983215332,"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/T10237","display_name":"Cryptography and Data Security","score":0.972599983215332,"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/T11130","display_name":"Coding theory and cryptography","score":0.9309999942779541,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9200000166893005,"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/homomorphic-encryption","display_name":"Homomorphic encryption","score":0.6794000267982483},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.6672999858856201},{"id":"https://openalex.org/keywords/interleaving","display_name":"Interleaving","score":0.6028000116348267},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.5587999820709229},{"id":"https://openalex.org/keywords/plaintext","display_name":"Plaintext","score":0.45089998841285706},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4325999915599823},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.35679998993873596},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.32659998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8105000257492065},{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.6794000267982483},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.6672999858856201},{"id":"https://openalex.org/C28034677","wikidata":"https://www.wikidata.org/wiki/Q17092530","display_name":"Interleaving","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C92717368","wikidata":"https://www.wikidata.org/wiki/Q1162538","display_name":"Plaintext","level":3,"score":0.45089998841285706},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.387800008058548},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2842999994754791},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C41458344","wikidata":"https://www.wikidata.org/wiki/Q732577","display_name":"Publication","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lanman66415.2025.11154574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lanman66415.2025.11154574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 31st International Symposium on Local and Metropolitan Area Networks (LANMAN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2603.02969","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.02969","pdf_url":"https://arxiv.org/pdf/2603.02969","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2603.02969","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.02969","pdf_url":"https://arxiv.org/pdf/2603.02969","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414199214.pdf","grobid_xml":"https://content.openalex.org/works/W4414199214.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W2046119925","https://openalex.org/W2112796928","https://openalex.org/W2159269332","https://openalex.org/W2473418344","https://openalex.org/W2768174108","https://openalex.org/W2912213068","https://openalex.org/W3003784440","https://openalex.org/W3016632787","https://openalex.org/W3175192640","https://openalex.org/W4402264897"],"related_works":[],"abstract_inverted_index":{"Federated":[0,81],"learning":[1,8,30,120],"(FL)":[2],"enables":[3],"collaborative":[4],"training":[5,89,97],"of":[6,28,85],"machine":[7],"models":[9],"without":[10],"sharing":[11],"sensitive":[12],"client":[13,132],"data,":[14,130],"making":[15],"it":[16,67],"a":[17],"cornerstone":[18],"for":[19],"privacy-critical":[20],"applications.":[21],"However,":[22],"FL":[23,58],"faces":[24],"the":[25,105,158],"dual":[26],"challenge":[27],"ensuring":[29],"quality":[31,121],"and":[32,72,95,103,107,113,137,141,172],"robust":[33,162],"privacy":[34,62,134,163],"protection":[35],"while":[36],"keeping":[37],"resource":[38],"consumption":[39],"low,":[40],"particularly":[41],"when":[42],"using":[43,63],"computationally":[44],"expensive":[45],"techniques":[46],"such":[47,156],"as":[48,157],"homomorphic":[49],"encryption":[50,140],"(HE).":[51],"In":[52],"this":[53],"work,":[54],"we":[55],"enhance":[56],"an":[57,73],"process":[59],"that":[60],"preserves":[61,131],"HE":[64],"by":[65,176],"integrating":[66],"with":[68,90,98,128,180],"synthetic":[69,99,114,129],"data":[70,92,100,133,153],"generation":[71],"interleaving":[74,146],"strategy.":[75,147],"Specifically,":[76],"our":[77,145,150],"solution,":[78],"named":[79],"Alternating":[80],"Learning":[82],"(Alt-FL),":[83],"consists":[84],"alternating":[86],"between":[87],"local":[88,96],"authentic":[91,112],"(authentic":[93],"rounds)":[94,102],"(synthetic":[101],"transferring":[104],"encrypted":[106],"plaintext":[108],"model":[109,123,170],"parameters":[110],"on":[111],"rounds":[115],"(resp.).":[116],"Our":[117],"approach":[118],"improves":[119],"(e.g.,":[122],"accuracy)":[124],"through":[125,144],"datasets":[126],"enhanced":[127],"via":[135],"HE,":[136],"keeps":[138],"manageable":[139],"decryption":[142],"costs":[143,175],"We":[148],"evaluate":[149],"solution":[151],"against":[152],"leakage":[154],"attacks,":[155],"DLG":[159],"attack,":[160],"demonstrating":[161],"protection.":[164],"Also,":[165],"Alt-FL":[166],"provides":[167],"13.4%":[168],"higher":[169],"accuracy":[171],"decreases":[173],"HE-related":[174],"up":[177],"to":[178,182],"48%":[179],"respect":[181],"Selective":[183],"HE.":[184]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}