{"id":"W1987500677","doi":"10.2196/ijmr.2140","title":"An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods","year":2012,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"k-anonymity; Computer science; Information loss; Information sensitivity; Mutual information; Entropy (arrow of time); Generalization; Private information retrieval; Data mining; Diversity (politics); Identifier; Conditional entropy; Anonymity; Row; Machine learning; Information retrieval; Artificial intelligence; Computer security; Mathematics; Database; Principle of maximum entropy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0119034,0.001434909,0.001647035,0.002480478,0.002577851,0.00280985,0.00299678,0.001988173,0.001379163],"category_scores_gemma":[0.03219709,0.0007420183,0.002546272,0.002773743,0.003994728,0.008746359,0.007089121,0.004676049,0.000453049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218227,"about_ca_system_score_gemma":0.002944233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009102885,"about_ca_topic_score_gemma":0.0005505945,"domain_scores_codex":[0.9779109,0.01132962,0.001162289,0.002438432,0.006253331,0.0009053887],"domain_scores_gemma":[0.9721658,0.01452457,0.002077928,0.007711629,0.002836577,0.0006835256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007670495,0.0004805636,0.004674385,0.0004443395,0.0004787788,0.0004013324,0.002319223,0.2108868,0.02017546,0.3932337,0.004295421,0.361843],"study_design_scores_gemma":[0.00007834151,0.0004453757,0.001319477,0.0001107163,0.0001363672,0.0008167476,0.0003177638,0.6058835,0.01995979,0.3620481,0.00871564,0.0001682207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007590269,0.0002336398,0.990448,0.0003975109,0.00002590189,0.00007604434,0.00005030981,0.0001283577,0.001049931],"genre_scores_gemma":[0.4297154,0.0006104801,0.5654972,0.0004874367,0.0002859952,0.0004908231,0.0002035659,0.0001495301,0.002559487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0119034,"threshold_uncertainty_score":0.06295198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1305208629525585,"score_gpt":0.4657120208107823,"score_spread":0.3351911578582237,"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."}}