{"id":"W3157038070","doi":"10.1109/jiot.2021.3077897","title":"Privacy Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"King Abdulaziz University","keywords":"Computer science; Scheme (mathematics); Computer network; Information privacy; Data collection; Deep learning; Artificial intelligence; Computer security","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":[0.002285038,0.0008218396,0.001046775,0.0006560129,0.0008460568,0.0009557551,0.002184129,0.001136612,0.0007202359],"category_scores_gemma":[0.005164285,0.000407762,0.0007226208,0.0009350325,0.001043122,0.003974791,0.003610098,0.002364049,0.0002516586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446985,"about_ca_system_score_gemma":0.001276767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001675434,"about_ca_topic_score_gemma":0.002077246,"domain_scores_codex":[0.9973615,0.0007690121,0.0002048628,0.0005075714,0.0007908603,0.0003662026],"domain_scores_gemma":[0.9961504,0.001222422,0.0005854208,0.001305205,0.0005413793,0.0001952252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002010129,0.0009954196,0.01241449,0.0003568833,0.0003513825,0.0006010606,0.0007569673,0.3477877,0.04241388,0.0323555,0.008995473,0.5509611],"study_design_scores_gemma":[0.00002046265,0.00009756201,0.000563646,0.000007012409,0.00001620916,0.00008705485,0.00003156233,0.9848873,0.007185294,0.006436376,0.0006496657,0.00001788526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08599342,0.0004007234,0.908446,0.0007742831,0.0000676724,0.0001572988,0.000228852,0.002623551,0.001308239],"genre_scores_gemma":[0.9330332,0.0001228152,0.06510442,0.0002813142,0.00003450205,0.0001111927,0.0002865782,0.00002485871,0.0010012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002285038,"threshold_uncertainty_score":0.01208454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05688638608537772,"score_gpt":0.3072392591663899,"score_spread":0.2503528730810122,"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."}}