{"id":"W7116407680","doi":"10.1109/tdsc.2025.3646011","title":"Efficient and Secure Data Sharing With Mobile Crowdsensing in Internet of Vehicles","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Data sharing; Information privacy; The Internet; Encryption; Single point of failure; Mobile device; Data security; Protocol (science); Bloom filter; Secure multi-party computation","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001209255,0.000536558,0.0007768646,0.0006829971,0.0005592328,0.0006413243,0.0009050677,0.0002611462,0.000007010974],"category_scores_gemma":[0.00001593136,0.0005277263,0.00007577849,0.0012278,0.0003142215,0.0002844918,0.0001683362,0.00104527,0.000001585822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008740374,"about_ca_system_score_gemma":0.0002064912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008393932,"about_ca_topic_score_gemma":0.0004746814,"domain_scores_codex":[0.9959619,0.0001868421,0.000920873,0.001808778,0.0003889516,0.0007326512],"domain_scores_gemma":[0.9974938,0.0005090857,0.0002888237,0.00137251,0.0001626794,0.00017309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002165824,0.0004936557,0.001261073,0.00104623,0.0002305351,0.0001620671,0.01042293,0.6418008,0.002205,0.0004669693,0.00003388749,0.3416603],"study_design_scores_gemma":[0.001367643,0.0002801062,0.0003562874,0.004531864,0.0001228673,0.0001818065,0.001113251,0.9793082,0.01211502,0.0000514711,0.0001056738,0.0004658293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5063837,0.001348587,0.4913304,0.00007546963,0.0003629089,0.0003040826,0.0000104824,0.00005253092,0.000131856],"genre_scores_gemma":[0.9899234,0.0001043476,0.009676383,0.0001003329,0.00003436253,0.000003950068,0.000003130029,0.00003524283,0.0001188719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4835398,"threshold_uncertainty_score":0.9997174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01858765233178488,"score_gpt":0.2587873833948896,"score_spread":0.2401997310631047,"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."}}