{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001442685,0.0001385267,0.0001756062,0.0001380102,0.0001303941,0.00008562794,0.0002405207,0.00004898912,0.0002549598],"category_scores_gemma":[0.000642515,0.0001420666,0.00001790713,0.0001171727,0.00008013425,0.001182911,0.0003112566,0.0002727914,0.00001707805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008707,"about_ca_system_score_gemma":0.0004928204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006334732,"about_ca_topic_score_gemma":0.0007341188,"domain_scores_codex":[0.9980977,0.000153603,0.0002696715,0.0006062905,0.0006730058,0.0001997917],"domain_scores_gemma":[0.9980214,0.00002581728,0.0001682641,0.000999987,0.0005269913,0.0002574984],"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":[0.002041899,0.002680021,0.1217129,0.000333843,0.0007914587,0.0003293988,0.004713606,0.0004400029,0.1665451,0.002058452,0.002939737,0.6954136],"study_design_scores_gemma":[0.002051114,0.0001924645,0.19814,0.0001313476,0.00008190047,0.0007372436,0.0002608561,0.7762862,0.0007826121,0.0001777082,0.02090494,0.0002535298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881084,0.00003879811,0.007023519,0.001259212,0.0007754859,0.0007712942,0.001671577,0.0001018251,0.0002499146],"genre_scores_gemma":[0.9748929,0.000007527541,0.01063637,0.0005586501,0.0009214663,0.00001737465,0.01291943,0.00002188303,0.00002439692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7758462,"threshold_uncertainty_score":0.5793309,"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."}}