{"id":"W2806094422","doi":"10.1145/3196494.3196536","title":"Entwining Sanitization and Personalization on Databases","year":2018,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Personalization; Computer science; Redistribution (election); Collusion; Database; Internet privacy; Context (archaeology); Computer security; World Wide Web; Business","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0001234856,0.00005832406,0.00004336581,0.00007720789,0.0001109142,0.00009436736,0.003437131,0.00002280378,0.00004828418],"category_scores_gemma":[0.004988756,0.00005187295,0.000005295254,0.0002174529,0.00007222006,0.0006281768,0.01533009,0.0000393958,0.00004389492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000015182,"about_ca_system_score_gemma":0.00001115863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001448967,"about_ca_topic_score_gemma":0.000007213463,"domain_scores_codex":[0.9994033,0.00001968223,0.00007085672,0.0002644984,0.0001331017,0.0001085383],"domain_scores_gemma":[0.9978064,0.0000661753,0.00003034725,0.002032692,0.00004383623,0.00002055568],"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":[0.000006269998,0.0000492147,0.006572102,0.00001939683,0.00001247585,0.000005328072,0.0003044832,0.000001068772,0.001923686,0.4891695,0.4089694,0.09296702],"study_design_scores_gemma":[0.0003600782,0.000280619,0.006040359,0.000104877,0.000005426098,0.00002275003,0.0001412849,0.8249065,0.04585516,0.09722403,0.02470586,0.0003530349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01010101,0.00002214672,0.9779728,0.004974501,0.0001313776,0.00005189826,0.000005426122,0.0006239435,0.006116873],"genre_scores_gemma":[0.5842075,0.0000289505,0.4148249,0.0007810305,0.00005783323,0.000003275703,0.00002767976,0.000004888157,0.00006393045],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249055,"threshold_uncertainty_score":0.9926338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05946027778985304,"score_gpt":0.3011858715523214,"score_spread":0.2417255937624684,"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."}}