{"id":"W1893507969","doi":"10.3233/jcs-140514","title":"<i>k</i> -jump: A strategy to design publicly-known algorithms for privacy preserving micro-data disclosure","year":2015,"lang":"en","type":"article","venue":"Journal of Computer Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Generalization; Jump; Algorithm; Adversarial system; Data mining; Artificial intelligence; Mathematics","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":[0.01109818,0.0008362656,0.001446298,0.001362919,0.001474361,0.003906786,0.004109482,0.002962961,0.002426934],"category_scores_gemma":[0.04075062,0.0007288612,0.001566181,0.001137529,0.004497417,0.008150811,0.006121609,0.003992168,0.0009021337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002519955,"about_ca_system_score_gemma":0.003167471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004610186,"about_ca_topic_score_gemma":0.0003540451,"domain_scores_codex":[0.9909277,0.003939635,0.0006272502,0.00171963,0.001876358,0.0009095213],"domain_scores_gemma":[0.9537452,0.02553809,0.003707791,0.01312435,0.002654174,0.001230269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001423005,0.0005657048,0.004624042,0.0003385845,0.0001829825,0.0001915283,0.0007677592,0.2039712,0.01577129,0.6027672,0.006289138,0.1631077],"study_design_scores_gemma":[0.00007800067,0.0001832756,0.0001931257,0.0000303012,0.00003285136,0.0001476856,0.00007340396,0.7751467,0.01135323,0.2111691,0.001554203,0.000038129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03358912,0.00009790643,0.9622635,0.0008213364,0.00003116839,0.000161447,0.0000481314,0.0009149328,0.002072413],"genre_scores_gemma":[0.6779681,0.0001131522,0.3189285,0.0003981536,0.00007069445,0.0002440666,0.0001150879,0.0002003346,0.00196189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01109818,"threshold_uncertainty_score":0.05869347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410498570909768,"score_gpt":0.336376961267048,"score_spread":0.1953271041760712,"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."}}