{"id":"W2953030092","doi":"10.1109/tdsc.2019.2922958","title":"Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Encryption; Decision tree; Outsourcing; Decision tree learning; Classifier (UML); Data mining; Server; Computer security; Machine learning; Artificial intelligence; Computer network","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":[0.001797388,0.0004375882,0.0006061612,0.0008052,0.001137704,0.001410613,0.001316565,0.0009112981,0.002879271],"category_scores_gemma":[0.005828922,0.0002284762,0.0006239601,0.001041483,0.0005806673,0.002470971,0.001805978,0.00124046,0.001499547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323809,"about_ca_system_score_gemma":0.001861562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001867249,"about_ca_topic_score_gemma":0.001525205,"domain_scores_codex":[0.9969435,0.0006865683,0.0003222644,0.0003786463,0.001266463,0.0004025041],"domain_scores_gemma":[0.9958103,0.00124477,0.000528292,0.001475813,0.0007452905,0.0001956636],"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":[0.00247606,0.0005883311,0.008555751,0.0002550212,0.0001533201,0.00110641,0.0005734978,0.0898994,0.04835466,0.05342773,0.01974271,0.7748672],"study_design_scores_gemma":[0.00009264036,0.000182219,0.001936765,0.0000376605,0.00004046825,0.0006648196,0.0001711968,0.92378,0.03043666,0.03390763,0.00870736,0.00004262028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06073713,0.0005594025,0.9320558,0.001030058,0.0001509862,0.0003479486,0.0004188512,0.002156855,0.00254297],"genre_scores_gemma":[0.8142841,0.0002946712,0.1816908,0.000204249,0.0001002715,0.0001834594,0.0008070022,0.00005908734,0.002376389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002879271,"threshold_uncertainty_score":0.009632111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549565481363617,"score_gpt":0.2747887559841842,"score_spread":0.249293101170548,"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."}}