{"id":"W3135019605","doi":"10.1145/3448016.3452812","title":"PCOR: Private Contextual Outlier Release via Differentially Private Search","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Royal Bank of Canada","keywords":"Outlier; Anomaly detection; Context (archaeology); Computer science; Population; Metric (unit); Differential privacy; Data mining; Artificial intelligence; Geography; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004471087,0.0009613315,0.00283238,0.001212698,0.001428792,0.002851809,0.005196034,0.002520838,0.004027963],"category_scores_gemma":[0.01903153,0.0005544241,0.001594732,0.003010492,0.002114061,0.005211558,0.0090225,0.002719898,0.001912006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001718017,"about_ca_system_score_gemma":0.004042769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357612,"about_ca_topic_score_gemma":0.002532698,"domain_scores_codex":[0.9932286,0.001726391,0.0004014803,0.001297492,0.002383801,0.0009622042],"domain_scores_gemma":[0.9895046,0.003061552,0.0007256113,0.005511341,0.0007786128,0.000418365],"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":[0.004241795,0.0006926994,0.006727763,0.0006317779,0.0004711899,0.001625922,0.0011223,0.2527841,0.0330603,0.1906935,0.03575491,0.4721938],"study_design_scores_gemma":[0.0002950133,0.0002895311,0.0009578799,0.0000355614,0.00008145007,0.0008920669,0.0002012608,0.8417982,0.009901797,0.1346219,0.01085307,0.00007223734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03293811,0.001164312,0.9538101,0.001343852,0.0001811726,0.0004101315,0.0009891666,0.004415185,0.004747941],"genre_scores_gemma":[0.7494596,0.0006865915,0.2384569,0.001136561,0.0003238605,0.0004835282,0.001828633,0.0004546991,0.007169662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005196034,"threshold_uncertainty_score":0.0236457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439339963994034,"score_gpt":0.2721658057903361,"score_spread":0.2477724061503957,"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."}}