{"id":"https://openalex.org/W7124896202","doi":"https://doi.org/10.1109/aiware69974.2025.00013","title":"How Does Quantization Impact Privacy Risk on LLMS for Code?","display_name":"How Does Quantization Impact Privacy Risk on LLMS for Code?","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W7124896202","doi":"https://doi.org/10.1109/aiware69974.2025.00013"},"language":null,"primary_location":{"id":"doi:10.1109/aiware69974.2025.00013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiware69974.2025.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 2nd IEEE/ACM International Conference on AI-powered Software (AIware)","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/A5123401045","display_name":"Md Nazmul Haque","orcid":null},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Md Nazmul Haque","raw_affiliation_strings":["North Carolina State University"],"affiliations":[{"raw_affiliation_string":"North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123390224","display_name":"Hua Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Yang","raw_affiliation_strings":["North Carolina State University"],"affiliations":[{"raw_affiliation_string":"North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123373032","display_name":"Zhou Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zhou Yang","raw_affiliation_strings":["University of Alberta"],"affiliations":[{"raw_affiliation_string":"University of Alberta","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103075477","display_name":"Bowen Xu","orcid":"https://orcid.org/0000-0001-5013-9009"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bowen Xu","raw_affiliation_strings":["North Carolina State University"],"affiliations":[{"raw_affiliation_string":"North Carolina State University","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5123401045"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71263801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"50","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.2867000102996826,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.2867000102996826,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10260","display_name":"Software Engineering Research","score":0.22679999470710754,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11986","display_name":"Scientific Computing and Data Management","score":0.1274999976158142,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6358000040054321},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.48739999532699585},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47850000858306885},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.46309998631477356},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.4474000036716461},{"id":"https://openalex.org/keywords/safeguarding","display_name":"Safeguarding","score":0.4318999946117401},{"id":"https://openalex.org/keywords/threat-model","display_name":"Threat model","score":0.37869998812675476},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3625999987125397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703000009059906},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6358000040054321},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.5648999810218811},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.47769999504089355},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.46309998631477356},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C2776743756","wikidata":"https://www.wikidata.org/wiki/Q5097921","display_name":"Safeguarding","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3174000084400177},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C169093310","wikidata":"https://www.wikidata.org/wiki/Q3702971","display_name":"Personally identifiable information","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2776945810","wikidata":"https://www.wikidata.org/wiki/Q17006654","display_name":"Data anonymization","level":3,"score":0.26649999618530273},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aiware69974.2025.00013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiware69974.2025.00013","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 2nd IEEE/ACM International Conference on AI-powered Software (AIware)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.49860331416130066}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1574447377","https://openalex.org/W2042571564","https://openalex.org/W2101105183","https://openalex.org/W2148190602","https://openalex.org/W2535690855","https://openalex.org/W2795435272","https://openalex.org/W3034940165","https://openalex.org/W3214437258","https://openalex.org/W4308410483","https://openalex.org/W4382463788","https://openalex.org/W4389208909","https://openalex.org/W4394744221","https://openalex.org/W4394769034","https://openalex.org/W4400582422","https://openalex.org/W4402665833","https://openalex.org/W4403761251","https://openalex.org/W4404781336","https://openalex.org/W4409362760","https://openalex.org/W4415800460"],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,83,208],"for":[3,44,69,236],"code":[4],"(LLMs4Code)":[5],"rely":[6],"heavily":[7],"on":[8,130,174],"massive":[9],"training":[10,57,91],"data,":[11,14,92],"including":[12],"sensitive":[13],"such":[15],"as":[16,40],"cloud":[17],"service":[18],"credentials":[19],"of":[20,27,77,111],"the":[21,28,75,89,126,176,181,203],"projects":[22],"and":[23,73,103,136,152,163,193,230],"personal":[24],"identifiable":[25],"information":[26],"developers,":[29],"raising":[30],"serious":[31],"privacy":[32,46,105,116,137,177,194,238],"concerns.":[33],"Membership":[34],"inference":[35],"(MI)":[36],"has":[37,170],"recently":[38],"emerged":[39],"an":[41,197],"effective":[42],"tool":[43],"assessing":[45],"risk":[47,138,178],"by":[48],"identifying":[49],"whether":[50,96],"specific":[51],"data":[52],"belong":[53],"to":[54,101,114,154,180],"a":[55,171,187],"model's":[56],"set.":[58],"In":[59,121],"parallel,":[60],"model":[61,157,228],"compression":[62],"techniques,":[63],"especially":[64],"quantization,":[65],"have":[66],"gained":[67],"traction":[68],"reducing":[70,175],"computational":[71],"costs":[72],"enabling":[74],"deployment":[76],"large":[78],"models.":[79,217],"However,":[80],"while":[81],"quantized":[82],"still":[84],"retain":[85,102],"knowledge":[86],"learned":[87],"from":[88],"original":[90,182],"it":[93],"remains":[94],"unclear":[95],"quantization":[97,132,149,169],"affects":[98],"their":[99],"ability":[100],"expose":[104],"information.":[106],"Answering":[107],"this":[108,122],"question":[109],"is":[110],"great":[112],"importance":[113],"understanding":[115],"risks":[117],"in":[118,140],"real-world":[119],"deployments.":[120],"work,":[123],"we":[124,145,201,219],"conduct":[125],"first":[127],"empirical":[128],"study":[129],"how":[131],"influences":[133],"task":[134,191],"performance":[135,192],"simultaneously":[139],"LLMs4Code.":[141,242],"To":[142],"do":[143],"this,":[144],"implement":[146],"widely":[147],"used":[148],"techniques":[150],"(static":[151],"dynamic)":[153],"four":[155],"representative":[156],"families,":[158],"namely":[159],"Pythia,":[160],"CodeGen,":[161],"GPT-Neo,":[162],"starcoder2.":[164],"Our":[165],"results":[166],"demonstrate":[167,220],"that":[168,205,221],"significant":[172],"impact":[173],"relative":[179],"model.":[183],"We":[184],"also":[185],"uncover":[186],"positive":[188],"correlation":[189],"between":[190],"risk,":[195],"indicating":[196],"underlying":[198],"trade-off.":[199],"Moreover,":[200],"reveal":[202],"possibility":[204],"quantizing":[206],"larger":[207],"could":[209],"yield":[210],"better":[211],"balance":[212],"than":[213],"using":[214],"full-precision":[215],"small":[216],"Finally,":[218],"these":[222],"findings":[223],"generalize":[224],"across":[225],"different":[226],"architectures,":[227],"sizes":[229],"MI":[231],"methods,":[232],"offering":[233],"practical":[234],"guidance":[235],"safeguarding":[237],"when":[239],"deploying":[240],"compressed":[241]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2026-01-21T00:00:00"}