{"id":"W2976195905","doi":"10.1109/isit.2019.8849381","title":"Synthesizing Differentially Private Datasets using Random Mixing","year":2019,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Variance (accounting); Noise (video); Data mining; Machine learning; Differential privacy; Mixing (physics); Artificial intelligence; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004535539,0.0001928582,0.0002600596,0.000159237,0.0001174905,0.0003574658,0.02724699,0.00009975297,0.0001023207],"category_scores_gemma":[0.004650756,0.000164625,0.00005829406,0.0003373605,0.00004552728,0.00127706,0.09448633,0.0002067985,0.000204626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000602941,"about_ca_system_score_gemma":0.00003713582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004987207,"about_ca_topic_score_gemma":0.000003923571,"domain_scores_codex":[0.9981848,0.00007792167,0.0002826736,0.0006548335,0.0003387454,0.000461021],"domain_scores_gemma":[0.9893616,0.0003057141,0.0001215772,0.0101296,0.00002312467,0.00005840936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001210262,0.0003633389,0.02143441,0.0003660989,0.0003967819,0.0001557466,0.0001831569,0.0003651775,0.6507664,0.06403922,0.1134501,0.1483585],"study_design_scores_gemma":[0.001087136,0.00002631396,0.0006071559,0.0001430537,0.00001310649,0.00003396595,0.0000124573,0.8542135,0.08497532,0.05480709,0.003610088,0.0004708289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1956326,0.00005644348,0.7998643,0.001867768,0.0004989345,0.0002460617,0.00002439264,0.001035485,0.0007740375],"genre_scores_gemma":[0.4403453,0.00001177192,0.5594189,0.0001370641,0.00002183861,0.000003414562,0.00002183458,0.00001334518,0.00002654003],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8538483,"threshold_uncertainty_score":0.9780161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0327809016945833,"score_gpt":0.2708401369970557,"score_spread":0.2380592353024724,"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."}}