{"id":"W2802882176","doi":"10.4018/ijmstr.2017100101","title":"Data Security and Privacy Assurance Considerations in Cloud Computing for Health Insurance Providers","year":2017,"lang":"en","type":"article","venue":"International Journal of Monitoring and Surveillance Technologies Research","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Cloud computing; Computer security; Vendor; Business; Data breach; Cloud computing security; Health care; Internet privacy; Information privacy; Computer science; Marketing","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003895427,0.00009716897,0.0002383358,0.000329713,0.0006215426,0.0009350023,0.002844,0.00006306384,1.558733e-7],"category_scores_gemma":[0.01079037,0.00009361217,0.00002254375,0.0001049215,0.0003847873,0.001074798,0.002368034,0.0005942244,2.773155e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254422,"about_ca_system_score_gemma":0.0002793679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001721529,"about_ca_topic_score_gemma":0.0002179252,"domain_scores_codex":[0.9981803,0.0001374681,0.0004477491,0.0003520703,0.0005636139,0.0003188461],"domain_scores_gemma":[0.9964774,0.001471045,0.0003868948,0.000884636,0.0007244735,0.00005555767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004308923,0.000103912,0.9070037,0.00007647866,0.00006856335,0.0000822579,0.001038166,0.00005248458,0.0002359155,0.008246717,0.002507813,0.0805409],"study_design_scores_gemma":[0.002521937,0.0003307948,0.8777926,0.0007929088,0.000001089487,0.0004958906,0.001222066,0.02907591,0.000698011,0.07987481,0.006851173,0.0003428323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.899199,0.003094136,0.01821102,0.07748105,0.001372173,0.0003116138,0.000226511,0.00008652091,0.00001799569],"genre_scores_gemma":[0.9752184,0.002451783,0.02211489,0.00001108199,0.000187863,0.00000556274,0.000003154599,0.000005356308,0.000001973699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08019806,"threshold_uncertainty_score":0.9975421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1506092897053812,"score_gpt":0.4505415027211259,"score_spread":0.2999322130157447,"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."}}