{"id":"https://openalex.org/W4412889956","doi":"https://doi.org/10.18653/v1/2025.acl-long.1220","title":"Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models","display_name":"Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889956","doi":"https://doi.org/10.18653/v1/2025.acl-long.1220"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1220","pdf_url":"https://aclanthology.org/2025.acl-long.1220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1220.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094077467","display_name":"James Flemings","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James Flemings","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056037931","display_name":"Bo Jiang","orcid":"https://orcid.org/0000-0003-4341-2032"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Jiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014759932","display_name":"Wanrong Zhang","orcid":"https://orcid.org/0000-0002-2393-2308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wanrong Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079191559","display_name":"Zafar Takhirov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zafar Takhirov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018033573","display_name":"Murali Annavaram","orcid":"https://orcid.org/0000-0002-4633-6867"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Murali Annavaram","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":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"25092","last_page":"25108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9577999711036682,"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":0.9577999711036682,"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":0.6872625946998596},{"id":"https://openalex.org/keywords/leakage","display_name":"Leakage (economics)","score":0.4610850214958191},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.4287719428539276},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4062363803386688},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.39987730979919434},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3501056134700775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6872625946998596},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.4610850214958191},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.4287719428539276},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4062363803386688},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.39987730979919434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3501056134700775},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1220","pdf_url":"https://aclanthology.org/2025.acl-long.1220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1220","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1220","pdf_url":"https://aclanthology.org/2025.acl-long.1220.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7461970842","display_name":null,"funder_award_id":"DGE-1842487","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8070878210","display_name":"Graduate Research Fellowship Program (GRFP)","funder_award_id":"1842487","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8682633387","display_name":"SHF: Small: ML Accelerator Cohort Architecture","funder_award_id":"2224319","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316785","display_name":"VMware","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889956.pdf","grobid_xml":"https://content.openalex.org/works/W4412889956.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3156288925","https://openalex.org/W2965236686","https://openalex.org/W2385771124","https://openalex.org/W1519912902","https://openalex.org/W2357840701","https://openalex.org/W3188962172","https://openalex.org/W2364814511","https://openalex.org/W2383951343","https://openalex.org/W2373563857","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Language":[0],"models":[1],"(LMs)":[2],"rely":[3],"on":[4,81],"their":[5],"parametric":[6,62,115,141],"knowledge":[7,12,22,63,95,116,133],"augmented":[8,68,155,187],"with":[9,138,186],"relevant":[10],"contextual":[11,21,69,94,132,188],"for":[13],"certain":[14],"tasks,":[15],"such":[16],"as":[17,162],"question":[18],"answering.However,":[19],"the":[20,52,56,60,67,90,105,113,118,151,182],"can":[23,54],"contain":[24,66],"private":[25],"information":[26],"that":[27,79,126],"may":[28],"be":[29],"leaked":[30],"when":[31,131],"answering":[32],"queries,":[33],"and":[34,157],"estimating":[35],"this":[36,71],"privacy":[37,57,86,91,128,152,171,183],"leakage":[38,92,129,153],"is":[39,134],"not":[40],"well":[41],"understood.A":[42],"straightforward":[43],"approach":[44,98],"of":[45,93,104,117,136,175,181],"directly":[46],"comparing":[47],"an":[48,108],"LM's":[49,61,109],"output":[50],"to":[51,88,140,154],"contexts":[53],"overestimate":[55],"risk,":[58],"since":[59],"might":[64],"already":[65],"knowledge.To":[70],"end,":[72],"we":[73,124,143,158],"introduce":[74],"context":[75,106,121,127,147,165,170],"influence,":[76],"a":[77,84],"metric":[78],"builds":[80],"differential":[82],"privacy,":[83],"widelyadopted":[85],"notion,":[87],"estimate":[89],"during":[96],"decoding.Our":[97],"effectively":[99],"measures":[100],"how":[101,146,160],"each":[102],"subset":[103],"influences":[107],"response":[110],"while":[111],"separating":[112],"specific":[114],"LM.Using":[119],"our":[120,176],"influence":[122,148],"metric,":[123],"demonstrate":[125,145],"occurs":[130],"out":[135],"distribution":[137],"respect":[139],"knowledge.Moreover,":[142],"experimentally":[144],"properly":[149],"attributes":[150],"contexts,":[156],"evaluate":[159],"factors-such":[161],"model":[163],"size,":[164,166],"generation":[167],"position,":[168],"etc.-affect":[169],"leakage.The":[172],"practical":[173],"implications":[174],"results":[177],"will":[178],"inform":[179],"practitioners":[180],"risk":[184],"associated":[185],"knowledge.":[189]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
