{"id":"https://openalex.org/W7135037692","doi":"https://doi.org/10.1109/jiot.2026.3672783","title":"PrivGITD: A High Accuracy and Privacy-Preserving Graph Convolutional Network for Insider Threat Detection","display_name":"PrivGITD: A High Accuracy and Privacy-Preserving Graph Convolutional Network for Insider Threat Detection","publication_year":2026,"publication_date":"2026-03-12","ids":{"openalex":"https://openalex.org/W7135037692","doi":"https://doi.org/10.1109/jiot.2026.3672783"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3672783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3672783","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5088015912","display_name":"Ximing Li","orcid":"https://orcid.org/0000-0002-2740-3470"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ximing Li","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2740-3470","affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029915936","display_name":"Huizheng Geng","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huizheng Geng","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122875631","display_name":"Linghui Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linghui Li","raw_affiliation_strings":["Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7614-3142","affiliations":[{"raw_affiliation_string":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128856260","display_name":"Fang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Liu","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100427223","display_name":"Chunmei Li","orcid":"https://orcid.org/0000-0002-0372-5803"},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunmei Li","raw_affiliation_strings":["China Mobile Research Institute, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Research Institute, Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24816963,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"11","first_page":"24171","last_page":"24184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.19280000030994415,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.19280000030994415,"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":0.14509999752044678,"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/T14347","display_name":"Big Data and Digital Economy","score":0.04639999940991402,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47369998693466187},{"id":"https://openalex.org/keywords/insider-threat","display_name":"Insider threat","score":0.4415000081062317},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.35260000824928284},{"id":"https://openalex.org/keywords/insider","display_name":"Insider","score":0.3508000075817108},{"id":"https://openalex.org/keywords/network-security","display_name":"Network security","score":0.33889999985694885},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3346000015735626}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8501999974250793},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C2776633304","wikidata":"https://www.wikidata.org/wiki/Q6038026","display_name":"Insider threat","level":3,"score":0.4415000081062317},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3880000114440918},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C2778971194","wikidata":"https://www.wikidata.org/wiki/Q1664551","display_name":"Insider","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33709999918937683},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.26989999413490295},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26460000872612},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3672783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3672783","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7089914363","display_name":null,"funder_award_id":"2023YFB3106404","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ensuring":[0],"data":[1,49],"security":[2,29],"is":[3],"a":[4,89,106,128,149],"fundamental":[5],"challenge":[6],"in":[7,69,79],"Internet":[8],"of":[9,16],"Things":[10],"(IoT)":[11],"deployments,":[12],"where":[13],"massive":[14],"numbers":[15],"interconnected":[17],"devices":[18],"continuously":[19],"transmit,":[20],"process,":[21],"and":[22,51,72,100,115,123,137,152,162],"store":[23],"sensitive":[24,80],"information.":[25],"Among":[26],"the":[27,36,65,73],"various":[28],"threats,":[30],"insider":[31,91],"threats":[32],"have":[33],"emerged":[34],"as":[35,40],"most":[37],"critical":[38],"risk,":[39],"malicious":[41],"insiders":[42],"with":[43],"authorized":[44],"access":[45],"can":[46],"directly":[47],"compromise":[48],"integrity":[50],"confidentiality.":[52],"Existing":[53],"detection":[54,61,93,175],"approaches":[55],"often":[56],"focus":[57],"solely":[58],"on":[59,96],"improving":[60],"accuracy,":[62],"while":[63,144,177],"neglecting":[64],"structural":[66],"information":[67],"embedded":[68],"user-device":[70],"interactions":[71],"imperative":[74],"need":[75],"for":[76],"privacy-preserving":[77,90],"mechanisms":[78],"environments.":[81],"To":[82],"address":[83],"these":[84],"challenges,":[85],"we":[86],"proposed":[87],"PrivGITD,":[88],"threat":[92],"framework":[94],"based":[95],"local":[97,122],"differential":[98],"privacy":[99,142],"graph":[101],"modularization.":[102],"Specifically,":[103],"PrivGITD":[104,169],"introduces":[105],"PageRank-based":[107],"Structural":[108],"Reconstruction":[109],"module":[110,135],"that":[111,168],"quantifies":[112],"node":[113],"importance":[114],"reconstructs":[116],"topological":[117],"embeddings":[118],"to":[119],"capture":[120],"both":[121],"global":[124],"interaction":[125],"patterns.":[126],"Additionally,":[127],"Local":[129],"Differential":[130],"Privacy":[131],"(LDP)-based":[132],"Feature":[133],"Encoding":[134],"perturbs":[136],"calibrates":[138],"user":[139],"features,":[140],"ensuring":[141],"protection":[143],"maintaining":[145],"statistical":[146],"utility.":[147],"Furthermore,":[148],"Label":[150],"Perturbation":[151],"Denoising":[153],"Learning":[154],"strategy":[155],"refines":[156],"randomized":[157],"labels":[158],"via":[159],"multi-hop":[160],"aggregation":[161],"soft":[163],"supervision.":[164],"Extensive":[165],"experiments":[166],"demonstrate":[167],"outperforms":[170],"state-of-the-art":[171],"methods,":[172],"achieving":[173],"higher":[174],"accuracy":[176],"effectively":[178],"preserving":[179],"privacy.":[180]},"counts_by_year":[],"updated_date":"2026-05-23T06:10:36.450269","created_date":"2026-03-13T00:00:00"}