{"id":"W4387367127","doi":"10.1287/opre.2022.0014","title":"Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Estimator; Data acquisition; Differential privacy; Scheme (mathematics); Mechanism design; Population; Bayesian probability; Machine learning; Payment; Artificial intelligence; Data mining; Statistics; Mathematics; World Wide Web","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":[0.01938503,0.001488592,0.004056181,0.00129451,0.001843974,0.005945047,0.005171464,0.004849384,0.004522535],"category_scores_gemma":[0.04924948,0.001804567,0.001773602,0.002315084,0.005688871,0.01370829,0.007252666,0.005523295,0.0009411963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00355901,"about_ca_system_score_gemma":0.00412392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006864609,"about_ca_topic_score_gemma":0.0005765785,"domain_scores_codex":[0.9823987,0.01048341,0.0007138549,0.002773013,0.002197398,0.001433605],"domain_scores_gemma":[0.9344121,0.0413808,0.006493999,0.01283569,0.003392259,0.001485167],"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":[0.001387809,0.0006241622,0.001969602,0.0003324518,0.0002584733,0.0002751717,0.0005778401,0.169045,0.004959263,0.7283691,0.005214524,0.0869867],"study_design_scores_gemma":[0.0003377361,0.0003511033,0.0005029726,0.00009018968,0.0000827133,0.0003082537,0.0001148999,0.5006397,0.004081557,0.4899926,0.003406405,0.00009181094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02024739,0.0007496268,0.9736739,0.001770792,0.0000718068,0.0002227807,0.000107404,0.0002936023,0.002862769],"genre_scores_gemma":[0.8016264,0.0008363153,0.1897622,0.0007203808,0.0003214355,0.0007474233,0.00013169,0.0001237172,0.005730442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01938503,"threshold_uncertainty_score":0.102519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119819214547237,"score_gpt":0.3737432342641646,"score_spread":0.2617613128094408,"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."}}