{"id":"https://openalex.org/W7164904451","doi":"https://doi.org/10.48550/arxiv.2606.16801","title":"The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models","display_name":"The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164904451","doi":"https://doi.org/10.48550/arxiv.2606.16801"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16801","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16801","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16801","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138739639","display_name":"Chen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103668258","display_name":"Gao X","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025295832","display_name":"Xianshun Wang","orcid":"https://orcid.org/0000-0002-4469-8426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xianshun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138745674","display_name":"Chengran Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chengran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044644099","display_name":"Shengyu Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Shengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138715465","display_name":"Xueluan Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Xueluan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023093685","display_name":"Linru Zhang","orcid":"https://orcid.org/0009-0001-2645-4479"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Linru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138725998","display_name":"Qian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138692660","display_name":"Kwok-Yan Lam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lam, Kwok-Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.6870999932289124,"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":0.6870999932289124,"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":0.08020000159740448,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.020600000396370888,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/obfuscation","display_name":"Obfuscation","score":0.5916000008583069},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.4844000041484833},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4221000075340271},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41679999232292175},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4081999957561493},{"id":"https://openalex.org/keywords/distributed-learning","display_name":"Distributed learning","score":0.3959999978542328},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.3885999917984009},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.38580000400543213},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3686999976634979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8406000137329102},{"id":"https://openalex.org/C40305131","wikidata":"https://www.wikidata.org/wiki/Q2616305","display_name":"Obfuscation","level":2,"score":0.5916000008583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5407000184059143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5113999843597412},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.4844000041484833},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3506999909877777},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2957000136375427},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16801","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16801","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16801","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16801","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Split":[0],"learning":[1,31,84,105,187],"provides":[2],"a":[3,20,35,79,118,123],"practical":[4],"paradigm":[5],"for":[6,86],"resource-constrained":[7],"users":[8],"to":[9,19,55,102,111,126,175],"train":[10],"Large":[11],"Language":[12],"Models":[13],"(LLMs)":[14],"by":[15],"offloading":[16],"computation-intensive":[17],"layers":[18],"server":[21],"while":[22,107],"keeping":[23],"raw":[24],"data":[25,57,192],"local.":[26],"However,":[27],"existing":[28,185],"privacy-preserving":[29,82,138],"split":[30,83,186],"methods":[32,46,189],"still":[33],"face":[34],"difficult":[36],"trade-off":[37],"among":[38],"utility,":[39],"privacy,":[40],"efficiency,":[41],"and":[42,63,94,132,151,163,195],"stability.":[43],"Specifically,":[44],"these":[45],"often":[47],"suffer":[48],"from":[49],"substantial":[50],"utility":[51,173],"degradation,":[52],"remain":[53],"vulnerable":[54],"advanced":[56],"reconstruction":[58,193],"attacks,":[59,194],"incur":[60],"prohibitive":[61],"computational":[62],"communication":[64],"overhead,":[65],"or":[66],"exhibit":[67],"unstable":[68],"performance":[69],"across":[70,156],"different":[71],"tasks.":[72],"In":[73],"this":[74,135],"paper,":[75],"we":[76],"propose":[77],"MIXGUARD,":[78],"novel":[80],"mixup-based":[81],"framework":[85],"LLMs.":[87],"MIXGUARD":[88,115,170],"introduces":[89],"token-level":[90],"obfuscation,":[91,93],"representation-level":[92],"adaptive":[95,199],"gradient":[96],"perturbation":[97],"mechanisms,":[98],"which":[99],"operate":[100],"jointly":[101],"preserve":[103],"useful":[104],"signals":[106],"preventing":[108],"privacy":[109,182],"leakage":[110],"the":[112,128],"server.":[113],"Technically,":[114],"first":[116],"constructs":[117],"lightweight":[119],"calibration":[120],"model":[121,136,160,172],"on":[122,140,147],"public":[124],"dataset":[125],"refine":[127],"approximated":[129],"target":[130],"representation,":[131],"then":[133],"applies":[134],"during":[137],"fine-tuning":[139,164],"private":[141],"data.":[142],"We":[143],"conduct":[144],"extensive":[145],"experiments":[146],"four":[148,152],"classification":[149],"tasks":[150,155],"text":[153],"generation":[154],"multiple":[157],"LLM":[158],"families,":[159],"sizes,":[161],"architectures,":[162],"strategies.":[165],"The":[166],"results":[167],"show":[168],"that":[169],"preserves":[171],"comparable":[174],"non-split":[176],"training":[177],"baselines,":[178],"consistently":[179],"achieves":[180],"stronger":[181],"protection":[183],"than":[184],"defense":[188],"against":[190],"state-of-the-art":[191],"remains":[196],"robust":[197],"under":[198],"attack":[200],"settings.":[201]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}
