{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004519401,0.0003777802,0.0006283463,0.0003157114,0.0001114234,0.0001303012,0.03104251,0.0003657239,0.000008205321],"category_scores_gemma":[0.001915676,0.0002983787,0.000166908,0.0007026974,0.0005305509,0.000293958,0.1017281,0.001022393,0.00001223707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002333643,"about_ca_system_score_gemma":0.0003450109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002318215,"about_ca_topic_score_gemma":0.0001173122,"domain_scores_codex":[0.9975745,0.000280123,0.0003518561,0.001114189,0.0001726423,0.0005066555],"domain_scores_gemma":[0.9888679,0.0008365709,0.0005039089,0.009580148,0.0001334409,0.00007807245],"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":[0.01079395,0.001685149,0.05140678,0.0009664571,0.002797898,0.0025826,0.002202582,0.311655,0.0004864555,0.588423,0.01676519,0.01023498],"study_design_scores_gemma":[0.008041934,0.0001856358,0.001319465,0.0005301359,0.00006001231,0.000006896607,0.0002909801,0.8761902,0.0007321168,0.1116477,0.0003024705,0.000692459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4330077,0.00005471637,0.5650994,0.0005006206,0.0004099072,0.0003662028,0.00001040443,0.0001923031,0.0003588049],"genre_scores_gemma":[0.9955896,0.0001661641,0.004077264,0.00002090682,0.000008285254,0.00000298558,0.000008808141,0.00002202506,0.000103926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5645351,"threshold_uncertainty_score":0.9999468,"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."}}