{"id":"W4238584846","doi":"10.32920/14639934","title":"Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"State Key Laboratory of Industrial Control Technology; Zhejiang University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Computer science; Data aggregator; Overhead (engineering); Encryption; Scheme (mathematics); Outsourcing; Information privacy; Cloud computing; Privacy software; Server; Computer network; Computer security; Secret sharing; Wireless sensor network; Cryptography; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002131185,0.0004972265,0.0008023252,0.0005752021,0.0007271868,0.001272105,0.001289414,0.000840871,0.0007624483],"category_scores_gemma":[0.005302805,0.0002377123,0.0006812055,0.001377362,0.0009158454,0.003197484,0.00298488,0.001097458,0.0002254292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008900617,"about_ca_system_score_gemma":0.0009207864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000541646,"about_ca_topic_score_gemma":0.0003502801,"domain_scores_codex":[0.9968379,0.0007963476,0.0002387022,0.0006050132,0.001145763,0.0003762456],"domain_scores_gemma":[0.9959715,0.001138681,0.0004786616,0.001656611,0.000598275,0.0001562311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001690946,0.0003646635,0.008307626,0.0004517973,0.0003167501,0.00144624,0.001358973,0.252655,0.1227132,0.2244407,0.007211038,0.3790431],"study_design_scores_gemma":[0.00006385304,0.0003341059,0.002014638,0.00001929426,0.00009007531,0.001253492,0.0002301025,0.8829117,0.04126713,0.0654493,0.006318026,0.00004827517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04828727,0.000326341,0.9486963,0.0003989862,0.00004869113,0.00007671442,0.0001319334,0.0003462028,0.001687551],"genre_scores_gemma":[0.9283973,0.0002138175,0.06947359,0.0001645081,0.0000959949,0.00005665087,0.0001514692,0.00002054063,0.001426143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002131185,"threshold_uncertainty_score":0.01127088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07456492369613038,"score_gpt":0.3223002923223938,"score_spread":0.2477353686262634,"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."}}