{"id":"W1980378234","doi":"10.1080/10106049.2010.496496","title":"Geomasking sensitive health data and privacy protection: an evaluation using an E911 database","year":2010,"lang":"en","type":"article","venue":"Geocarto International","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; National Institutes of Health","keywords":"Anonymity; k-anonymity; Geography; Census; Population; Computer science; Privacy protection; Database; Statistics; Data mining; Internet privacy; Computer security; Mathematics; Demography","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":[0.01634518,0.0007153354,0.0009278189,0.001214223,0.0009831754,0.002119182,0.001770306,0.001339416,0.0005892096],"category_scores_gemma":[0.03769022,0.0002604731,0.0004700455,0.001746506,0.0008613039,0.002655219,0.001773565,0.0006788879,0.000177805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375761,"about_ca_system_score_gemma":0.001179297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01106668,"about_ca_topic_score_gemma":0.005304408,"domain_scores_codex":[0.9850534,0.007194782,0.001340413,0.001320903,0.004626557,0.0004639954],"domain_scores_gemma":[0.9643238,0.02419841,0.001619789,0.005344641,0.003723618,0.0007897512],"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":[0.02897527,0.006054147,0.1787508,0.00268145,0.001679281,0.001602132,0.002725628,0.1288465,0.03660934,0.009056619,0.0228843,0.5801347],"study_design_scores_gemma":[0.003091878,0.01119512,0.1412426,0.0002682784,0.0007407594,0.003458397,0.002173375,0.7661547,0.05225078,0.002937317,0.01625658,0.0002302473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983084,0.0008931676,0.009878219,0.0006227043,0.00007071088,0.0006691906,0.001138846,0.001000662,0.002642428],"genre_scores_gemma":[0.9687384,0.0003865371,0.02759269,0.0001918925,0.0000295458,0.0001651696,0.002241792,0.00004025889,0.0006137094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01634518,"threshold_uncertainty_score":0.08644259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1314893698665452,"score_gpt":0.4104678790224628,"score_spread":0.2789785091559177,"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."}}