{"id":"W4411535570","doi":"10.3390/jcp5030036","title":"Enhancing User Experience with Visual Controls for Local Differential Privacy","year":2025,"lang":"en","type":"article","venue":"Journal of Cybersecurity and Privacy","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Differential privacy; Computer science; Usability; Human–computer interaction; Privacy software; Key (lock); Interface (matter); Control (management); Internet privacy; Information privacy; User interface; Computer security; Access control; Artificial intelligence; Data mining","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004957121,0.0002262694,0.0004619535,0.0002554361,0.0002241414,0.0003221107,0.008883486,0.0001501701,0.000009709444],"category_scores_gemma":[0.005679051,0.000174033,0.0001126707,0.0003412915,0.0002618389,0.001412174,0.01232999,0.0004553355,9.359856e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007812068,"about_ca_system_score_gemma":0.0002179011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001432691,"about_ca_topic_score_gemma":0.000009751678,"domain_scores_codex":[0.9981926,0.00007985168,0.0005883483,0.0003682927,0.0003768519,0.0003940362],"domain_scores_gemma":[0.9968371,0.000561323,0.0004150725,0.001800099,0.0002653284,0.0001210272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006174627,0.003329286,0.03355223,0.001629945,0.002528116,0.000742365,0.02718625,0.00006472674,0.05795592,0.314686,0.1254509,0.4266996],"study_design_scores_gemma":[0.01677987,0.003488894,0.01765757,0.002130834,0.0003102805,0.0006939393,0.001684599,0.07004226,0.1781097,0.6201607,0.08729582,0.001645547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3419655,0.0002941714,0.6510704,0.006054962,0.0003141046,0.0001885615,0.000003534264,0.00006300733,0.00004576987],"genre_scores_gemma":[0.8693622,0.00009857288,0.1301434,0.0002718907,0.000076964,0.00001370211,0.000001083861,0.000008171093,0.00002406558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5273967,"threshold_uncertainty_score":0.9964789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388713114239003,"score_gpt":0.2922476238218866,"score_spread":0.2783604926794966,"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."}}