{"id":"W2248830513","doi":"10.1109/kaleidoscope.2015.7383624","title":"Privacy, consumer trust and big data: Privacy by design and the 3 C'S","year":2015,"lang":"en","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Safeguard; Big data; Internet privacy; Consumer privacy; Information privacy; Computer security; Business; Computer science; Scale (ratio); Privacy by Design; Democracy; Political science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154396,0.00008071015,0.0001410013,0.00001571667,0.0003543684,0.0002854873,0.0004801504,0.00005383512,0.00004277173],"category_scores_gemma":[0.0006750763,0.00004548872,0.000009889603,0.00009329768,0.0008785462,0.0003901639,0.0003307303,0.00007500088,0.00001386071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001033394,"about_ca_system_score_gemma":0.0001334169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004130513,"about_ca_topic_score_gemma":0.000245217,"domain_scores_codex":[0.9989681,0.0002764645,0.000113613,0.0002201009,0.0002244581,0.0001972225],"domain_scores_gemma":[0.9990355,0.0004006896,0.00004728086,0.0003032536,0.00004900556,0.0001642864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004694652,0.0001083941,0.05576165,0.00001204783,0.0001725526,0.000008743128,0.02496033,0.000001175094,0.00003001413,0.130816,0.4729458,0.3147138],"study_design_scores_gemma":[0.003383915,0.00002122867,0.001369843,0.000003802517,0.00005491593,0.000002426358,0.002816493,0.0008359007,0.00001805361,0.009682731,0.9816481,0.0001625484],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3016834,0.03644183,0.1208798,0.1673925,0.002497401,0.005721971,0.0001602227,0.000700947,0.3645219],"genre_scores_gemma":[0.9943221,0.0006121443,0.0007733065,0.0006483606,0.0001801195,0.00001153521,0.000005120418,0.00000601247,0.003441253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6926388,"threshold_uncertainty_score":0.6244127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1246143593046335,"score_gpt":0.3267068249980526,"score_spread":0.2020924656934191,"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."}}