{"id":"W4312535937","doi":"10.14778/3551793.3551805","title":"Don't be a tattle-tale","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inference; Computation; Data mining; Information sensitivity; Information retrieval; Computer security; Algorithm; Artificial intelligence","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":[0.009080102,0.0008700519,0.001703155,0.001121659,0.005211788,0.006722107,0.002949878,0.004092019,0.02253168],"category_scores_gemma":[0.04984461,0.001079334,0.002723533,0.001499081,0.01171448,0.02552476,0.007598502,0.008158378,0.006624053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002061758,"about_ca_system_score_gemma":0.00271989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003666226,"about_ca_topic_score_gemma":0.003180029,"domain_scores_codex":[0.9925864,0.002470357,0.0003541925,0.002293049,0.001449553,0.0008464182],"domain_scores_gemma":[0.9732675,0.01447361,0.0009071183,0.008474756,0.001989335,0.0008877903],"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":[0.0002637342,0.00008581422,0.001831699,0.0001929108,0.0001374782,0.0003754527,0.001446824,0.00684992,0.001002797,0.9016235,0.04305195,0.04313802],"study_design_scores_gemma":[0.00003965079,0.00006010937,0.0002025926,0.00006395254,0.00004533724,0.0003529527,0.0003416119,0.01967455,0.00117256,0.9294212,0.0485682,0.00005720598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04443265,0.002651173,0.7642689,0.105386,0.002732843,0.000354815,0.00132227,0.002702529,0.07614885],"genre_scores_gemma":[0.6275264,0.002363579,0.2402623,0.02589482,0.001551099,0.0007585378,0.001460534,0.001823595,0.09835906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02253168,"threshold_uncertainty_score":0.07537603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477296912415364,"score_gpt":0.2453487926756373,"score_spread":0.2205758235514837,"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."}}