{"id":"W3131030483","doi":"10.1109/mnet.011.2000666","title":"Data Management for Future Wireless Networks: Architecture, Privacy Preservation, and Regulation","year":2021,"lang":"en","type":"article","venue":"IEEE Network","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information privacy; Architecture; Data sharing; Blockchain; Data management; Computer security; Privacy by Design; Wireless; Database; Telecommunications","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":["open_science"],"category_scores_codex":[0.0005086415,0.0001938921,0.0002054004,0.00005493042,0.0002519962,0.0003024653,0.01509322,0.0001302215,0.000001743066],"category_scores_gemma":[0.0006159196,0.0001919484,0.00003253182,0.0007679529,0.00005566688,0.0009001493,0.04541701,0.000204799,0.000001873222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002765522,"about_ca_system_score_gemma":0.00003788492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002352922,"about_ca_topic_score_gemma":0.00002551255,"domain_scores_codex":[0.9979919,0.00008143901,0.0002600421,0.0009507053,0.0002632555,0.0004526091],"domain_scores_gemma":[0.9880765,0.0002393032,0.0001429207,0.01137489,0.0001043627,0.00006204547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001726689,0.00003168394,0.0003117968,0.00007490189,0.00007227265,0.00001354102,0.00003814404,0.008933363,0.00001288272,0.0112977,0.8681108,0.1110856],"study_design_scores_gemma":[0.0002603456,0.0000165958,0.002217202,0.00005961977,0.0000173775,0.00001194349,0.000007321732,0.5050139,0.00006129963,0.3162803,0.1758779,0.0001761846],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002782838,0.001270523,0.9744893,0.01695699,0.003102167,0.0005155533,0.00003947032,0.0005598151,0.0002832904],"genre_scores_gemma":[0.02741673,0.000842648,0.9629453,0.000826404,0.006720035,0.0001331916,0.0006996075,0.00004380564,0.0003722206],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6922329,"threshold_uncertainty_score":0.9902356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675351936604162,"score_gpt":0.2746755675864966,"score_spread":0.237922048220455,"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."}}