{"id":"W2963817128","doi":"10.29012/jpc.724","title":"INSPECTRE: Privately Estimating the Unseen","year":2020,"lang":"en","type":"article","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo; Office of Naval Research; Simons Institute for the Theory of Computing, University of California Berkeley; Microsoft Research; National Science Foundation","keywords":"Sublinear function; Differential privacy; Sample (material); Entropy (arrow of time); Computer science; Mathematics; Sample size determination; Sensitivity (control systems); Statistics; Algorithm; Discrete mathematics; Engineering; Physics","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":[0.008073115,0.001320776,0.001807305,0.001187988,0.0008047068,0.003205639,0.004285326,0.002480033,0.003063469],"category_scores_gemma":[0.04396931,0.0009740253,0.001378781,0.001163169,0.002975,0.009491664,0.005919293,0.005015874,0.001186826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002197005,"about_ca_system_score_gemma":0.00172948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297152,"about_ca_topic_score_gemma":0.001420359,"domain_scores_codex":[0.9935477,0.002391606,0.0002200215,0.001337295,0.002136867,0.0003663457],"domain_scores_gemma":[0.9708495,0.01609534,0.001832501,0.00950273,0.001286161,0.0004337853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001283312,0.0003231101,0.009630009,0.0004065279,0.0003089783,0.000442564,0.000682366,0.3332859,0.02107461,0.3587143,0.01128293,0.2625655],"study_design_scores_gemma":[0.00002896954,0.00009864106,0.0007143739,0.00003639174,0.00002683364,0.0003138185,0.00004850315,0.8424491,0.00910815,0.143643,0.003493733,0.0000383917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009954401,0.000263728,0.9871796,0.0005528565,0.0000338168,0.00004798778,0.0002622815,0.0005865801,0.00111879],"genre_scores_gemma":[0.5364914,0.0006874562,0.452932,0.0009815088,0.000348111,0.0003244668,0.001414687,0.0004330779,0.006387281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008073115,"threshold_uncertainty_score":0.04269528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04265814773859074,"score_gpt":0.2861341317512873,"score_spread":0.2434759840126965,"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."}}