{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0002022909,0.0001273993,0.0001391401,0.00008283996,0.0001070295,0.0002496954,0.0115647,0.00009127474,0.00001190202],"category_scores_gemma":[0.00409578,0.0001140995,0.00002924129,0.0002485954,0.00003686506,0.0005243479,0.06793237,0.0001214661,0.00000573663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003637128,"about_ca_system_score_gemma":0.00003678468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001302595,"about_ca_topic_score_gemma":0.000113603,"domain_scores_codex":[0.9986249,0.00002959609,0.0001950716,0.0007211193,0.0001965502,0.0002327745],"domain_scores_gemma":[0.9894994,0.0001180334,0.00006575212,0.01020253,0.00006536351,0.00004889354],"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":[0.00005629097,0.001097152,0.01840469,0.0001430473,0.0002382224,0.000147763,0.00003362842,0.00001531967,0.07341407,0.0369484,0.08832435,0.781177],"study_design_scores_gemma":[0.001011919,0.0003440477,0.07919284,0.00004670726,0.0001026062,0.0000535394,0.00001785769,0.6039721,0.2472467,0.04995077,0.01748989,0.0005710631],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1874795,0.00002947254,0.8078042,0.003780172,0.0002605485,0.0001982173,0.00006653017,0.0003385195,0.00004283617],"genre_scores_gemma":[0.6175405,0.00004208792,0.3819363,0.0001512202,0.00002828868,0.00002847858,0.00007780113,0.00001081106,0.0001845808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.780606,"threshold_uncertainty_score":0.9937832,"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."}}