{"id":"W4226248253","doi":"10.48550/arxiv.2112.04640","title":"Differentially Private Ensemble Classifiers for Data Streams","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data stream mining; Concept drift; Computer science; Competitor analysis; Data stream; Private information retrieval; STREAMS; Data mining; Ensemble forecasting; Black box; Regression; Artificial intelligence; Machine learning; Mathematics; Statistics; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008230285,0.001003534,0.002202243,0.001134114,0.001060372,0.002316562,0.002626418,0.002168059,0.00155817],"category_scores_gemma":[0.02250376,0.0005562031,0.00104474,0.001621713,0.001227938,0.006142091,0.003458449,0.004905657,0.0007900369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601345,"about_ca_system_score_gemma":0.001543666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112115,"about_ca_topic_score_gemma":0.00109672,"domain_scores_codex":[0.9951932,0.001596978,0.0002946767,0.001007388,0.00152849,0.0003792344],"domain_scores_gemma":[0.9869915,0.006498692,0.0009845058,0.00395956,0.001227052,0.0003388626],"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":[0.0007595972,0.0003201066,0.004475329,0.0001401188,0.0002064293,0.0002215988,0.0003026893,0.5602571,0.004907227,0.09708942,0.009080904,0.3222395],"study_design_scores_gemma":[0.00001352143,0.0000293557,0.0001458243,0.000008891754,0.00001338949,0.00003605167,0.00001297838,0.957357,0.001514169,0.03982336,0.001038843,0.000006626463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0182425,0.0007190307,0.9782345,0.0006580589,0.00008770608,0.00005370925,0.0001971799,0.0008270718,0.0009801902],"genre_scores_gemma":[0.7457228,0.0009732649,0.2457161,0.0006229115,0.0006097359,0.0002690121,0.001209067,0.0002104387,0.004666648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008230285,"threshold_uncertainty_score":0.04352641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1662111201897946,"score_gpt":0.234368954785548,"score_spread":0.06815783459575336,"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."}}