{"id":"https://openalex.org/W7127964728","doi":"https://doi.org/10.48550/arxiv.2602.04927","title":"PriMod4AI: Lifecycle-Aware Privacy Threat Modeling for AI Systems using LLM","display_name":"PriMod4AI: Lifecycle-Aware Privacy Threat Modeling for AI Systems using LLM","publication_year":2026,"publication_date":"2026-02-04","ids":{"openalex":"https://openalex.org/W7127964728","doi":"https://doi.org/10.48550/arxiv.2602.04927"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.04927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.04927","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2602.04927","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125245793","display_name":"Gautam Savaliya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savaliya, Gautam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092737549","display_name":"Robert Aufschl\u00e4ger","orcid":"https://orcid.org/0009-0004-0986-3504"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aufschl\u00e4ger, Robert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125178109","display_name":"Abhishek Subedi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subedi, Abhishek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052070648","display_name":"Michael Heigl","orcid":"https://orcid.org/0000-0001-7303-113X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heigl, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056684537","display_name":"Martin Schramm","orcid":"https://orcid.org/0000-0001-6206-2969"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schramm, Martin","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.15320000052452087,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.15320000052452087,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1378999948501587,"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":0.13449999690055847,"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/information-privacy","display_name":"Information privacy","score":0.6159999966621399},{"id":"https://openalex.org/keywords/privacy-software","display_name":"Privacy software","score":0.5526000261306763},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.5235999822616577},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5152999758720398},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4875999987125397},{"id":"https://openalex.org/keywords/privacy-by-design","display_name":"Privacy by Design","score":0.41760000586509705},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.35429999232292175},{"id":"https://openalex.org/keywords/threat-model","display_name":"Threat model","score":0.33649998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7767000198364258},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.6159999966621399},{"id":"https://openalex.org/C509729295","wikidata":"https://www.wikidata.org/wiki/Q7246032","display_name":"Privacy software","level":3,"score":0.5526000261306763},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.5235999822616577},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.46810001134872437},{"id":"https://openalex.org/C193934123","wikidata":"https://www.wikidata.org/wiki/Q7246028","display_name":"Privacy by Design","level":3,"score":0.41760000586509705},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.31679999828338623},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C102938260","wikidata":"https://www.wikidata.org/wiki/Q1999831","display_name":"Privacy policy","level":3,"score":0.2994000017642975},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C3017597292","wikidata":"https://www.wikidata.org/wiki/Q25052250","display_name":"Privacy protection","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2776945810","wikidata":"https://www.wikidata.org/wiki/Q17006654","display_name":"Data anonymization","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.04927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.04927","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.04927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.04927","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"systems":[2,30,161],"introduce":[3],"complex":[4],"privacy":[5,21,36,59,65,86,137,170,176],"risks":[6],"throughout":[7],"their":[8],"lifecycle,":[9],"especially":[10],"when":[11],"processing":[12],"sensitive":[13],"or":[14],"high-dimensional":[15],"data.":[16],"Beyond":[17],"the":[18,26,80,192],"seven":[19],"traditional":[20],"threat":[22,66,148],"categories":[23,171],"defined":[24],"by":[25],"LINDDUN":[27,46,54,76],"framework,":[28],"AI":[29,160],"are":[31,97],"also":[32],"exposed":[33],"to":[34,126],"model-centric":[35,85,175],"attacks":[37],"such":[38],"as":[39,186],"membership":[40],"inference":[41],"and":[42,56,83,106,120,135,146,154],"model":[43],"inversion,":[44],"which":[45],"does":[47],"not":[48],"cover.":[49],"To":[50],"address":[51],"both":[52,152],"classical":[53,153,169],"threats":[55,91,138],"additional":[57],"AI-driven":[58,155],"attacks,":[60],"PriMod4AI":[61,117,164],"introduces":[62],"a":[63,75,84,100],"hybrid":[64],"modeling":[67],"approach":[68],"that":[69,150,163],"unifies":[70],"two":[71,159],"structured":[72],"knowledge":[73,77,88,95],"sources,":[74],"base":[78,89],"representing":[79],"established":[81],"taxonomy,":[82],"attack":[87],"capturing":[90],"outside":[92],"LINDDUN.":[93],"These":[94],"bases":[96],"embedded":[98],"into":[99],"vector":[101],"database":[102],"for":[103],"semantic":[104],"retrieval":[105],"combined":[107],"with":[108],"system":[109],"level":[110],"metadata":[111],"derived":[112],"from":[113],"Data":[114,121],"Flow":[115,122],"Diagram.":[116],"uses":[118],"retrieval-augmented":[119],"specific":[123],"prompt":[124],"generation":[125],"guide":[127],"large":[128],"language":[129],"models":[130],"(LLMs)":[131],"in":[132,188,191],"identifying,":[133],"explaining,":[134],"categorizing":[136],"across":[139,184],"lifecycle":[140],"stages.":[141],"The":[142,178],"framework":[143,179],"produces":[144,180],"justified":[145],"taxonomy-grounded":[147],"assessments":[149],"integrate":[151],"perspectives.":[156],"Evaluation":[157],"on":[158],"indicates":[162],"provides":[165],"broad":[166],"coverage":[167],"of":[168],"while":[172],"additionally":[173],"identifying":[174],"threats.":[177],"consistent,":[181],"knowledge-grounded":[182],"outputs":[183],"LLMs,":[185],"reflected":[187],"agreement":[189],"scores":[190],"observed":[193],"range.":[194]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-07T00:00:00"}