{"id":"W4233841865","doi":"10.1109/glocom.2014.7417592","title":"Lightweight Security and Privacy-Preserving Scheme for V2G Connection","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vehicle-to-grid; Computer science; Scheme (mathematics); Computer security; Grid; Electricity; Authentication (law); Smart grid; Computer network; Trusted third party; Confidentiality; Pseudonym; Power (physics); Electric vehicle; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001870397,0.0008874641,0.001345164,0.00127044,0.00241393,0.002292219,0.002107521,0.002058645,0.003228739],"category_scores_gemma":[0.004528924,0.0004103868,0.001161387,0.001628658,0.001602683,0.006230987,0.006197091,0.002544126,0.001164922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419803,"about_ca_system_score_gemma":0.0019121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008940655,"about_ca_topic_score_gemma":0.0005168535,"domain_scores_codex":[0.9953243,0.001306754,0.0004221972,0.0006234145,0.001546726,0.0007766787],"domain_scores_gemma":[0.9957705,0.0008782527,0.0006278786,0.001902644,0.000557312,0.0002634035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003001658,0.0006508209,0.004084209,0.0008665806,0.000375994,0.002332525,0.002158646,0.1090464,0.1058374,0.4768204,0.01675508,0.2780703],"study_design_scores_gemma":[0.0004362867,0.0007504015,0.001131841,0.00009800732,0.0002081605,0.002915233,0.000410275,0.7589746,0.03951995,0.1629019,0.03234939,0.0003039015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06290652,0.001084516,0.9211973,0.001107704,0.0003403255,0.0006942074,0.0003670542,0.002023833,0.01027856],"genre_scores_gemma":[0.9411631,0.0003750005,0.05241658,0.0002682755,0.000142742,0.0002960644,0.0003171468,0.00004812246,0.004973098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003228739,"threshold_uncertainty_score":0.01080126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067841135837278,"score_gpt":0.2677802557128315,"score_spread":0.2471018443544588,"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."}}