{"id":"W4407128147","doi":"10.1109/jiot.2025.3538679","title":"Toward Lightweight and Privacy-Preserving Data Provision in Digital Forensics for Driverless Taxi","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Information privacy; Digital forensics; Computer security; Computer forensics; Computer network; Internet privacy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003653285,0.0001489474,0.0002529605,0.0001934497,0.00004167876,0.0009091234,0.002653779,0.00006686365,0.000001195353],"category_scores_gemma":[0.0002352087,0.0001203588,0.0000693547,0.0001888847,0.00007850215,0.004125258,0.001965773,0.000250065,0.000001125836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004145737,"about_ca_system_score_gemma":0.00009906288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002239141,"about_ca_topic_score_gemma":0.000005919433,"domain_scores_codex":[0.9986721,0.00001217316,0.0004657494,0.0003367097,0.0002595392,0.000253754],"domain_scores_gemma":[0.9988589,0.0001311575,0.0002326388,0.0005187277,0.0001763889,0.00008215017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002032748,0.0003003189,0.01140262,0.0004482849,0.0002156947,0.000133622,0.009885622,0.00003541589,0.0007505812,0.07675568,0.07077141,0.8290974],"study_design_scores_gemma":[0.002687092,0.0005549194,0.001761915,0.002795042,0.00004076516,0.0004052615,0.0002857244,0.28168,0.02544734,0.6525179,0.03118349,0.000640625],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3020733,0.0002581933,0.6915015,0.001721808,0.00135074,0.0002142446,0.00001750303,0.00003514212,0.002827545],"genre_scores_gemma":[0.9727625,0.00002548965,0.02616415,0.0002147496,0.00006118036,0.000002148979,0.000006087408,0.000008626629,0.0007550184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8284569,"threshold_uncertainty_score":0.8766698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0298714678559844,"score_gpt":0.268823355274392,"score_spread":0.2389518874184076,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}