{"id":"W3102045859","doi":"10.1109/access.2020.3036811","title":"Preserving Privacy in Mobile Health Systems Using Non-Interactive Zero-Knowledge Proof and Blockchain","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"mHealth; Computer science; Computer security; Bluetooth; Mobile device; Authentication (law); Information privacy; Internet privacy; Wearable computer; Encryption; Cryptography; Wearable technology; Health care; Wireless; World Wide Web; Telecommunications; Embedded system","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":[0.00501329,0.0007492304,0.001112584,0.001027216,0.001879975,0.003602533,0.001864711,0.002059928,0.004471813],"category_scores_gemma":[0.01431172,0.000514092,0.001114953,0.001559544,0.004568208,0.007579267,0.006234779,0.002483909,0.001073753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830497,"about_ca_system_score_gemma":0.003509854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001660094,"about_ca_topic_score_gemma":0.001131146,"domain_scores_codex":[0.9916468,0.003526072,0.000659166,0.001110702,0.002094195,0.0009631542],"domain_scores_gemma":[0.9848358,0.008951399,0.001259066,0.003402879,0.000989466,0.000561461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007161287,0.000162548,0.001292337,0.0004024621,0.0001110725,0.001213252,0.001260052,0.1452802,0.009364983,0.7744174,0.002565864,0.06321372],"study_design_scores_gemma":[0.0001892534,0.0001520205,0.0001699538,0.0001022993,0.00004328632,0.0003996222,0.0001102421,0.3658035,0.007906128,0.6150485,0.01000071,0.00007453948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03843102,0.0004954406,0.950057,0.001632834,0.0001148546,0.0003093746,0.0002197375,0.0004643251,0.008275487],"genre_scores_gemma":[0.9083248,0.000654194,0.08422285,0.0002407483,0.0001145281,0.0004184486,0.0002175291,0.00006510736,0.005741651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00501329,"threshold_uncertainty_score":0.0265131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0671686621393793,"score_gpt":0.3570720891210807,"score_spread":0.2899034269817015,"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."}}