{"id":"W2166825417","doi":"10.1016/j.cose.2006.08.003","title":"A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration","year":2006,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Computer security; Artificial intelligence","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":[0.005765402,0.000744804,0.001667824,0.002230019,0.003282889,0.004203619,0.004068719,0.002285556,0.001628947],"category_scores_gemma":[0.01357437,0.0008617793,0.002446058,0.004047668,0.002751109,0.006666578,0.006446779,0.003215302,0.001137827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001755169,"about_ca_system_score_gemma":0.003969264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919086,"about_ca_topic_score_gemma":0.002089927,"domain_scores_codex":[0.9905528,0.003333515,0.0006412666,0.001726849,0.003227898,0.0005176809],"domain_scores_gemma":[0.9878327,0.002696241,0.0007852018,0.006535124,0.00173823,0.0004125294],"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":[0.0005542041,0.0004444526,0.002959928,0.0002496112,0.0003438062,0.0003178496,0.001506913,0.1453067,0.02314011,0.529618,0.008898403,0.2866601],"study_design_scores_gemma":[0.00003688671,0.00009849391,0.0005088122,0.00002802008,0.00007885219,0.0003351005,0.000280755,0.6642327,0.01599785,0.3102443,0.008097528,0.00006078402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00339511,0.00006187669,0.9955133,0.0002781766,0.00001667815,0.00005225727,0.00006538473,0.0001804244,0.0004368295],"genre_scores_gemma":[0.1833744,0.000232484,0.8125779,0.0002763035,0.0001103104,0.0002324047,0.0004192006,0.0001148599,0.002662248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005765402,"threshold_uncertainty_score":0.03049076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02851437046450045,"score_gpt":0.27683647893019,"score_spread":0.2483221084656896,"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."}}