{"id":"W4408325434","doi":"10.1109/tifs.2025.3550064","title":"COKV: Key-Value Data Collection With Condensed Local Differential Privacy","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of New Brunswick","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Computer science; Key (lock); Data collection; Information privacy; Value (mathematics); Computer security; Data mining; Statistics; Mathematics","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":[0.004964032,0.0006610816,0.00149828,0.001246887,0.001562286,0.002947239,0.003146045,0.001439873,0.002215876],"category_scores_gemma":[0.02149068,0.0005819094,0.0009382138,0.003377507,0.002476863,0.009194363,0.009407873,0.002599105,0.0009510869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592202,"about_ca_system_score_gemma":0.002499883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008632204,"about_ca_topic_score_gemma":0.0006836108,"domain_scores_codex":[0.9897922,0.003035313,0.0007991175,0.00167192,0.003858471,0.0008428989],"domain_scores_gemma":[0.9847651,0.003867561,0.00124169,0.007956785,0.001754456,0.0004144603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001527784,0.0003259014,0.005846752,0.0007171479,0.000332192,0.0006808711,0.001745572,0.1029102,0.04353288,0.3792757,0.01582505,0.44728],"study_design_scores_gemma":[0.0001947215,0.0003668721,0.001260823,0.00008859943,0.0001002518,0.001581277,0.0005443559,0.6458552,0.04742607,0.2810056,0.02138968,0.0001866799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01290544,0.0003851714,0.9832792,0.0003307075,0.0000732476,0.0002231321,0.0002583078,0.001006341,0.001538392],"genre_scores_gemma":[0.722891,0.0004668748,0.2709145,0.0005587437,0.0001629134,0.0004914537,0.0008940343,0.000229382,0.003391207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004964032,"threshold_uncertainty_score":0.02625269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603542634979655,"score_gpt":0.2490755708345017,"score_spread":0.2330401444847051,"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."}}