{"id":"W385637060","doi":"10.22215/etd/2015-10845","title":"Geographic Partitioning Techniques for the Anonymization of Health Care Data","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Data anonymization; Identifiability; Confidentiality; Voronoi diagram; Computer science; Data mining; Data aggregator; Data science; Health care; Computer security; Information privacy; Wireless sensor network; Machine learning","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":[0.001779254,0.000379297,0.0004225236,0.001450967,0.0009927233,0.001194635,0.0006881367,0.0004983941,0.001677171],"category_scores_gemma":[0.007422015,0.0002458582,0.0006388029,0.001968955,0.0007446226,0.001575148,0.002067376,0.0006797178,0.0004970343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000722534,"about_ca_system_score_gemma":0.000999197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002502651,"about_ca_topic_score_gemma":0.003474876,"domain_scores_codex":[0.9971573,0.001485643,0.0001470695,0.0002970532,0.0008052796,0.0001076409],"domain_scores_gemma":[0.9965036,0.001712409,0.0003081107,0.001135559,0.0002762055,0.00006414622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004132139,0.0001217722,0.003615118,0.0004532335,0.0002539854,0.0003901031,0.002375751,0.2211966,0.02737986,0.2342228,0.007237752,0.5023397],"study_design_scores_gemma":[0.00009320966,0.0002110307,0.003016461,0.00008774704,0.00008734462,0.001162029,0.0009809089,0.7162731,0.03467507,0.1563275,0.08700261,0.00008313453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01220506,0.0003237959,0.9844237,0.0001514401,0.00003279667,0.0001488879,0.0001691168,0.0007965117,0.001748715],"genre_scores_gemma":[0.2250272,0.00050674,0.7720996,0.00006961528,0.00003844645,0.0001747668,0.0005814173,0.0001516671,0.001350554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002502651,"threshold_uncertainty_score":0.009409726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1207403037942895,"score_gpt":0.3637338457388296,"score_spread":0.2429935419445401,"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."}}