{"id":"W4200171302","doi":"10.1002/spy2.202","title":"Limiting sensitive values in an anonymized table while reducing information loss via <i>p</i>‐proportion","year":2021,"lang":"en","type":"article","venue":"Security and Privacy","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mondrian; Computer science; Categorical variable; Metric (unit); Information loss; Data mining; Set (abstract data type); Table (database); Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00598289,0.00089828,0.001533654,0.001902017,0.001310035,0.003584788,0.002694955,0.001178483,0.002171519],"category_scores_gemma":[0.02368527,0.0004082414,0.001188623,0.003013476,0.001928049,0.006447672,0.004013126,0.002097153,0.0006191322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193178,"about_ca_system_score_gemma":0.002181773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002014551,"about_ca_topic_score_gemma":0.00145029,"domain_scores_codex":[0.9901763,0.003187282,0.0006383843,0.001722024,0.003576267,0.0006997859],"domain_scores_gemma":[0.9801498,0.006772976,0.001994712,0.008971135,0.001720859,0.0003905606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001060193,0.0003258422,0.004541512,0.0001744149,0.000139186,0.0002641692,0.0005470299,0.4353049,0.01397224,0.1189246,0.00469653,0.4200494],"study_design_scores_gemma":[0.0000353644,0.0002695013,0.0007227968,0.00003704351,0.00005398884,0.0007867599,0.0002559513,0.9012901,0.02265017,0.06919262,0.004653967,0.00005166333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05512828,0.0002870721,0.9408521,0.0003997292,0.0000412572,0.000113668,0.0001426441,0.0008153044,0.002220007],"genre_scores_gemma":[0.7105376,0.0003138164,0.2856037,0.0002363151,0.0000777572,0.0001298003,0.0003576482,0.000145371,0.002597918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00598289,"threshold_uncertainty_score":0.03164095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027458052035916,"score_gpt":0.2596319537574042,"score_spread":0.2393573732370451,"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."}}