{"id":"W2906042702","doi":"10.1109/tvt.2018.2888854","title":"A Privacy-Preserving and Verifiable Querying Scheme in Vehicular Fog Data Dissemination","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Science and Technology Department of Zhejiang Province; Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Research Foundation Singapore; New Brunswick Innovation Foundation","keywords":"Computer science; Paillier cryptosystem; Dissemination; Computer network; Homomorphic encryption; Correctness; Vehicular ad hoc network; Scheme (mathematics); Unavailability; Cryptosystem; Security analysis; Encryption; Computer security; Wireless; Wireless ad hoc network; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003872541,0.0003361529,0.000361844,0.0007388233,0.000206051,0.00006506,0.0008606263,0.0005508044,0.00006530889],"category_scores_gemma":[0.00004130224,0.0003806316,0.00005161743,0.001155587,0.0002280019,0.0004848382,0.00003933585,0.0008425005,0.00006112135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001689523,"about_ca_system_score_gemma":0.00002888569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004391687,"about_ca_topic_score_gemma":0.0002917469,"domain_scores_codex":[0.9979646,0.00005098102,0.0004033905,0.0006760384,0.000255195,0.0006498205],"domain_scores_gemma":[0.9980587,0.00007345602,0.00005062714,0.001643592,0.00007362737,0.0001000254],"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":[0.00008408837,0.0003987277,0.001055275,0.0003885416,0.0004770558,0.0003289045,0.0005658161,0.6156111,0.2710602,0.0004853649,0.001757163,0.1077877],"study_design_scores_gemma":[0.0005201522,0.00009042022,0.0001527058,0.0002684178,0.0000417934,0.00009118646,0.0001005193,0.929756,0.06259429,0.0003436946,0.005645801,0.0003949766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7196652,0.0008029718,0.2771291,0.0004477751,0.0003558217,0.000424936,0.00002330634,0.0009329578,0.0002178851],"genre_scores_gemma":[0.9916719,0.0003062689,0.007640542,0.00003660225,0.00006799782,0.0001013655,0.00002717637,0.00008969202,0.00005849313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3141449,"threshold_uncertainty_score":0.9998646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360050991089529,"score_gpt":0.2504390070049739,"score_spread":0.2368384970940786,"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."}}