{"id":"W2073874433","doi":"10.1016/j.datak.2008.06.007","title":"Data privacy protection in multi-party clustering","year":2008,"lang":"en","type":"article","venue":"Data & Knowledge Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"McMaster University","keywords":"Cluster analysis; Privacy protection; Internet privacy; Computer security; Data Protection Act 1998; Information privacy; Computer science; Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.01119543,0.0005343956,0.001963206,0.001465868,0.00363688,0.005339757,0.004030563,0.003733231,0.001937031],"category_scores_gemma":[0.04045887,0.001103205,0.001874931,0.003445924,0.003510257,0.009960263,0.008182185,0.004365894,0.0007699479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002307156,"about_ca_system_score_gemma":0.002394963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008215068,"about_ca_topic_score_gemma":0.0005722588,"domain_scores_codex":[0.9817858,0.007947638,0.001112522,0.002529364,0.005363334,0.00126118],"domain_scores_gemma":[0.9476671,0.01914139,0.002821391,0.02737561,0.002338246,0.0006561311],"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":[0.001252253,0.0002685916,0.005275837,0.0003632187,0.0003269753,0.0006337591,0.001791185,0.1944567,0.01033148,0.6305198,0.005455856,0.1493243],"study_design_scores_gemma":[0.00005242116,0.0000776892,0.000769203,0.00005707192,0.00007357935,0.0007135443,0.0003670599,0.5060968,0.01517906,0.4717212,0.00484672,0.00004558296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03608429,0.0004377217,0.9578888,0.001636445,0.00006459554,0.00008893322,0.0002285891,0.0002959636,0.0032746],"genre_scores_gemma":[0.8778353,0.0003266303,0.1174221,0.0003474134,0.0001137024,0.0001345496,0.0002943512,0.00008857095,0.003437403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01119543,"threshold_uncertainty_score":0.05920774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1658117584570718,"score_gpt":0.315461514927751,"score_spread":0.1496497564706792,"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."}}