{"id":"W2995459148","doi":"10.4230/lipics.itc.2020.14","title":"The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Israel Science Foundation; Ben-Gurion University of the Negev; National Science Foundation; VMware; Natural Sciences and Engineering Research Council of Canada; Georgetown University; University of Alberta","keywords":"Differential privacy; Computer science; Task (project management); Protocol (science); Focus (optics); Differential (mechanical device); Simple (philosophy); Range (aeronautics); Power (physics); Bridge (graph theory); Human–computer interaction; Data mining; Engineering","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":[0.01280928,0.001003966,0.002098274,0.001217081,0.00197241,0.003386108,0.003200897,0.002766889,0.003492689],"category_scores_gemma":[0.02798333,0.001091636,0.001924661,0.001370058,0.01096751,0.01189605,0.01336384,0.004885869,0.001201813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001633687,"about_ca_system_score_gemma":0.001901857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005391789,"about_ca_topic_score_gemma":0.0005193125,"domain_scores_codex":[0.9843957,0.009858631,0.0003940572,0.002064798,0.0025239,0.0007628748],"domain_scores_gemma":[0.9628372,0.01841921,0.002210758,0.01396129,0.001203463,0.001368138],"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":[0.0004369208,0.0001363002,0.001535651,0.000173373,0.0001363274,0.0004504979,0.001202427,0.06954617,0.01076209,0.8919832,0.001277781,0.0223591],"study_design_scores_gemma":[0.0001473928,0.0004051576,0.0002910492,0.0000553245,0.00009471222,0.0004020468,0.0002191469,0.3736063,0.00824932,0.6094781,0.006981128,0.00007042202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02379848,0.0002267597,0.9674566,0.001231566,0.00003242155,0.00007826239,0.00002949574,0.0002861316,0.006860359],"genre_scores_gemma":[0.8294201,0.0003290758,0.1636519,0.0005956989,0.0001281618,0.0003300081,0.00004009161,0.0001418982,0.005363103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01280928,"threshold_uncertainty_score":0.06774276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972300945924784,"score_gpt":0.1865275295618498,"score_spread":0.1468045201026019,"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."}}