{"id":"W2088969708","doi":"10.1109/icde.2010.5447898","title":"On optimal anonymization for l<sup>+</sup>-diversity","year":2010,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Pruning; Computer science; Enumeration; Novelty; Metric (unit); Data publishing; Diversity (politics); Upper and lower bounds; Mathematical optimization; Theoretical computer science; Algorithm; Mathematics; Artificial intelligence; Discrete mathematics; Publishing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005162415,0.0002249004,0.0002016447,0.0002186215,0.0004755972,0.0002227048,0.02269956,0.0002832353,0.0001673833],"category_scores_gemma":[0.009214807,0.0002103973,0.00008948084,0.0004700326,0.0001350391,0.001116896,0.06992947,0.0003279615,0.0001724394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006639661,"about_ca_system_score_gemma":0.00006278385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002339543,"about_ca_topic_score_gemma":0.00003916215,"domain_scores_codex":[0.9980316,0.00003223079,0.000227591,0.0007862681,0.0004292233,0.0004931377],"domain_scores_gemma":[0.9925852,0.0003212524,0.0001086491,0.006714979,0.0001682313,0.000101714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003669639,0.0001735008,0.000624795,0.0000225249,0.00003187787,0.00001356955,0.0001125566,0.0005558539,0.003417473,0.1560115,0.819253,0.01974667],"study_design_scores_gemma":[0.0007393522,0.0002397815,0.0006857436,0.00001314827,0.00001115391,0.00001192418,0.00001226867,0.8481167,0.0109091,0.119113,0.01973658,0.0004112098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09061711,0.00001041629,0.8891726,0.01219508,0.0006726047,0.0004508297,0.0000520185,0.001804333,0.005025027],"genre_scores_gemma":[0.3153161,0.000009133221,0.6836035,0.0004287541,0.00006959039,0.00001981945,0.00003466835,0.00001400994,0.0005044194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8475609,"threshold_uncertainty_score":0.999131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02494798375215538,"score_gpt":0.2536782843203048,"score_spread":0.2287303005681494,"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."}}