{"id":"W3177379628","doi":"10.1109/bigdatasecurityhpscids52275.2021.00025","title":"User and Event Behavior Analytics on Differentially Private Data for Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Differential privacy; Anomaly detection; Computer science; Analytics; Outsourcing; Computer security; Digitization; Data analysis; Data mining","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":[0.004327958,0.000617001,0.00106969,0.001509981,0.000935368,0.002253171,0.001773739,0.001232839,0.000848181],"category_scores_gemma":[0.01803153,0.0004278296,0.0007389703,0.00247357,0.001877015,0.006204076,0.003369113,0.001887973,0.0005209948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001796893,"about_ca_system_score_gemma":0.001384846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009251447,"about_ca_topic_score_gemma":0.0006301359,"domain_scores_codex":[0.9919721,0.002435984,0.000453646,0.001368311,0.003214261,0.0005557935],"domain_scores_gemma":[0.9841038,0.005061917,0.001809741,0.00737061,0.001341067,0.0003129217],"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":[0.002563904,0.0009133415,0.04441383,0.0002822596,0.0004115351,0.001352245,0.00191804,0.1904417,0.09277543,0.2156814,0.005455606,0.4437907],"study_design_scores_gemma":[0.0000307668,0.0001440034,0.002638817,0.00001674161,0.00003216194,0.0004688803,0.0001476302,0.89065,0.03716369,0.06557873,0.003089195,0.00003929624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06682655,0.0001840903,0.929081,0.000554857,0.00003842805,0.0001120098,0.0002949382,0.001855535,0.00105253],"genre_scores_gemma":[0.8895187,0.0001141998,0.1088079,0.0001627556,0.00004814756,0.00008346967,0.0003310959,0.00006044427,0.0008732068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004327958,"threshold_uncertainty_score":0.02288866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06073699179841111,"score_gpt":0.3103829372246732,"score_spread":0.2496459454262621,"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."}}