{"id":"https://openalex.org/W4406496891","doi":"https://doi.org/10.1109/bigdata62323.2024.10826053","title":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","display_name":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406496891","doi":"https://doi.org/10.1109/bigdata62323.2024.10826053"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10826053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008977566","display_name":"Alycia N. Carey","orcid":"https://orcid.org/0000-0002-3587-4088"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alycia N. Carey","raw_affiliation_strings":["University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046988949","display_name":"Karuna Bhaila","orcid":null},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karuna Bhaila","raw_affiliation_strings":["University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057146172","display_name":"Kennedy Edemacu","orcid":"https://orcid.org/0000-0001-9877-9216"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kennedy Edemacu","raw_affiliation_strings":["University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008463509","display_name":"Xintao Wu","orcid":"https://orcid.org/0000-0002-2823-3063"},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xintao Wu","raw_affiliation_strings":["University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA"],"affiliations":[{"raw_affiliation_string":"University of Arkansas,Electrical Engineering and Computer Science,Fayetteville,AR,USA","institution_ids":["https://openalex.org/I78715868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008977566"],"corresponding_institution_ids":["https://openalex.org/I78715868"],"apc_list":null,"apc_paid":null,"fwci":3.1328,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92940032,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1552","last_page":"1557"},"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/T10237","display_name":"Cryptography and Data Security","score":0.9983999729156494,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9447000026702881,"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/computer-science","display_name":"Computer science","score":0.6704036593437195},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6312063336372375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35383278131484985}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6704036593437195},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6312063336372375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35383278131484985},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10826053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10826053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310058","display_name":"University of Arkansas","ror":"https://ror.org/05jbt9m15"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1968752677","https://openalex.org/W2013823004","https://openalex.org/W2398203045","https://openalex.org/W2473418344","https://openalex.org/W2785361959","https://openalex.org/W2810715221","https://openalex.org/W2955197576","https://openalex.org/W3006678129","https://openalex.org/W3014705052","https://openalex.org/W3035231859","https://openalex.org/W3123375411","https://openalex.org/W3188505388","https://openalex.org/W3207429447","https://openalex.org/W3213407400","https://openalex.org/W4205228770","https://openalex.org/W4221159672","https://openalex.org/W4231844697","https://openalex.org/W4281764334","https://openalex.org/W4283031227","https://openalex.org/W4285171808","https://openalex.org/W4286909613","https://openalex.org/W4302305417","https://openalex.org/W4306178203","https://openalex.org/W4307001389","https://openalex.org/W4307937235","https://openalex.org/W4367189670","https://openalex.org/W4368304560","https://openalex.org/W4378498814","https://openalex.org/W4378510501","https://openalex.org/W4381586841","https://openalex.org/W4384918448","https://openalex.org/W4385567149","https://openalex.org/W4386977463","https://openalex.org/W4387596222","https://openalex.org/W4389500732","https://openalex.org/W4404569891","https://openalex.org/W4404782964","https://openalex.org/W4406496891","https://openalex.org/W6712394148","https://openalex.org/W6747732332","https://openalex.org/W6752985224","https://openalex.org/W6765516245","https://openalex.org/W6778883912","https://openalex.org/W6799631579","https://openalex.org/W6802224358","https://openalex.org/W6802279528","https://openalex.org/W6802709103","https://openalex.org/W6810332117","https://openalex.org/W6838540236","https://openalex.org/W6838995493","https://openalex.org/W6845810429","https://openalex.org/W6845816046","https://openalex.org/W6846328044","https://openalex.org/W6846967249","https://openalex.org/W6852567367","https://openalex.org/W6852927443","https://openalex.org/W6853004024","https://openalex.org/W6853341658","https://openalex.org/W6854866820","https://openalex.org/W6856218604","https://openalex.org/W6857700311","https://openalex.org/W6859063301","https://openalex.org/W6874517714"],"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":{"In-context":[0],"learning":[1],"(ICL)":[2],"enables":[3],"large":[4],"language":[5,41],"models":[6],"(LLMs)":[7],"to":[8,10,25,29,69,97,120,173],"adapt":[9],"new":[11],"tasks":[12],"by":[13,35],"conditioning":[14],"on":[15],"demonstrations":[16],"of":[17,80,109,164],"question-answer":[18],"pairs.":[19],"Recently,":[20],"ICL":[21,75,115,128,159],"has":[22,55],"been":[23,56],"extended":[24],"allow":[26],"tabular":[27,61,71,99,114,167],"data":[28,53,62,72,100,117,168],"be":[30,95],"used":[31,73,101],"as":[32,85],"demonstration":[33],"examples":[34],"serializing":[36],"individual":[37,148],"records":[38,149],"into":[39,89,147],"natural":[40],"formats.":[42],"However,":[43],"it":[44,54],"is":[45,76],"well-known":[46],"that":[47,157],"LLMs":[48],"can":[49,94,160],"leak":[50],"information":[51],"from":[52],"prompted":[57],"on,":[58],"and":[59,122,139],"since":[60],"often":[63],"contain":[64],"sensitive":[65],"information,":[66],"understanding":[67],"how":[68,90],"protect":[70,98,161],"in":[74,102,134],"a":[77],"critical":[78],"area":[79],"research.":[81],"This":[82],"work":[83],"serves":[84],"an":[86],"initial":[87],"investigation":[88],"differential":[91],"privacy":[92,132,163,179],"(DP)":[93],"utilized":[96],"ICL.":[103],"Specifically,":[104],"we":[105],"investigate":[106],"the":[107,136,162,165],"application":[108],"DP":[110,142],"mechanisms":[111],"for":[112],"private":[113,127],"via":[116,144],"privatization":[118],"prior":[119],"serialization":[121],"prompting.":[123],"We":[124],"formulate":[125],"two":[126],"frameworks":[129],"with":[130],"provable":[131],"guarantees":[133],"both":[135],"local":[137],"(LDP-TabICL)":[138],"global":[140],"(GDP-TabICL)":[141],"scenarios":[143],"injecting":[145],"noise":[146],"or":[150],"group":[151],"statistics,":[152],"respectively.":[153],"Our":[154],"evaluations":[155],"show":[156],"DP-based":[158],"underlying":[166],"while":[169],"achieving":[170],"comparable":[171],"performance":[172],"non-LLM":[174],"baselines,":[175],"especially":[176],"under":[177],"high":[178],"regimes.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-28T08:17:26.163206","created_date":"2025-10-10T00:00:00"}