{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00933261,0.0005161276,0.0005429853,0.001329462,0.002159921,0.006147151,0.00231488,0.002519102,0.001972701],"category_scores_gemma":[0.01294494,0.0006010263,0.0006149834,0.002850541,0.003618927,0.0147234,0.00460531,0.003970795,0.0008006109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002588286,"about_ca_system_score_gemma":0.003743791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001978702,"about_ca_topic_score_gemma":0.001570193,"domain_scores_codex":[0.9937448,0.002301739,0.000593754,0.0007781197,0.002150011,0.0004315901],"domain_scores_gemma":[0.9902086,0.00354841,0.001102536,0.003041446,0.001784568,0.0003144392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007360001,0.00006576493,0.00157055,0.0004515406,0.00003791105,0.0001486313,0.0006122374,0.0219102,0.004048342,0.836853,0.006379937,0.1278484],"study_design_scores_gemma":[0.00003592304,0.00009329234,0.0007344647,0.000509554,0.00005236161,0.0004790685,0.0004822874,0.1468472,0.009409626,0.6572958,0.1839869,0.00007363594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01285631,0.005606931,0.9517664,0.009222385,0.0003219542,0.0003304717,0.0001913485,0.0005836288,0.0191207],"genre_scores_gemma":[0.5480996,0.01774594,0.4149314,0.002603777,0.0008248765,0.001102683,0.000865784,0.0002319508,0.01359395],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00933261,"threshold_uncertainty_score":0.04935616,"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."}}