{"id":"W4291653259","doi":"10.1109/tmc.2022.3199048","title":"HealthFort: A Cloud-Based eHealth System With Conditional Forward Transparency and Secure Provenance via Blockchain","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Putian University; National Natural Science Foundation of China","keywords":"Computer science; Computer security; Cloud computing; eHealth; Encryption; Transparency (behavior); Forward secrecy; Confidentiality; Delegate; Internet privacy; Key (lock); Verifiable secret sharing; Authentication (law); Delegation; Secrecy; Smart contract; Public-key cryptography; Server; Password; World Wide Web; Blockchain; Health care","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004765996,0.0002417341,0.0002991413,0.0002315486,0.001844697,0.00004221257,0.0005587991,0.00009124913,0.000009781184],"category_scores_gemma":[7.33143e-7,0.0002431646,0.00007662185,0.000830833,0.0001229799,0.00005722076,0.000009919969,0.0007465411,0.000003279317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002399561,"about_ca_system_score_gemma":0.0002500198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007336296,"about_ca_topic_score_gemma":0.00004202567,"domain_scores_codex":[0.9978765,0.0001508723,0.0004142352,0.0007288993,0.0003833452,0.0004461081],"domain_scores_gemma":[0.9988177,0.0001641191,0.0002087954,0.0005850582,0.0000786852,0.000145611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000705098,0.0007868666,0.0001221916,0.0003686485,0.00006211906,0.00003434724,0.001496864,0.8842944,0.0001379072,0.05339432,0.00006917962,0.05916267],"study_design_scores_gemma":[0.001068715,0.001084014,0.00009114057,0.00006592459,0.00001750774,0.0002635739,0.0003070013,0.9939906,0.0009536252,0.00103425,0.0008056619,0.0003179968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06274586,0.0001663994,0.9339526,0.001010662,0.0002560757,0.001069294,0.00008871855,0.0006765741,0.00003378052],"genre_scores_gemma":[0.9841026,0.000003430258,0.01445305,0.00041602,0.00003359023,0.0009492196,0.000008087344,0.00002188367,0.00001209488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9213567,"threshold_uncertainty_score":0.9994547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006905866799278625,"score_gpt":0.2242207163075572,"score_spread":0.2173148495082786,"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."}}