{"id":"W3157195642","doi":"10.1016/j.inffus.2021.04.017","title":"Differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems: State-of-the-art and perspectives","year":2021,"lang":"en","type":"article","venue":"Information Fusion","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Differential privacy; Cyber-physical system; Computer science; Cyber Space; Field (mathematics); Data science; Deep learning; Big data; Artificial intelligence; State (computer science); Sensor fusion; Computer security; Data mining; World Wide Web; The Internet","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":[0.007315905,0.001139066,0.002398138,0.0009842138,0.0006964653,0.004460477,0.003089078,0.002484673,0.001819875],"category_scores_gemma":[0.009876125,0.0006036827,0.001361364,0.001841499,0.003090854,0.007692014,0.004498438,0.004705055,0.0003142695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003040196,"about_ca_system_score_gemma":0.002713017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003327152,"about_ca_topic_score_gemma":0.00184179,"domain_scores_codex":[0.9965248,0.001503364,0.0001689625,0.0006232007,0.0008001662,0.0003795452],"domain_scores_gemma":[0.9936988,0.004066578,0.0004205164,0.000920769,0.0006672576,0.0002259799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002850622,0.0001402858,0.001654013,0.0004089489,0.0003224887,0.0001225771,0.0002460169,0.2998625,0.001547639,0.5768521,0.003635597,0.1149227],"study_design_scores_gemma":[0.000007926658,0.00003804074,0.0001915906,0.00003662944,0.00002672815,0.00004133562,0.0000344191,0.7726461,0.0007963935,0.2243718,0.001790366,0.00001876595],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.008471101,0.003817265,0.9829396,0.002695922,0.0001003681,0.00003381336,0.0001304048,0.0001296016,0.001682],"genre_scores_gemma":[0.8363655,0.007603901,0.1491479,0.0009895605,0.0007584654,0.0001303208,0.0004396402,0.00008878736,0.004475958],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007315905,"threshold_uncertainty_score":0.03869069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690557045381842,"score_gpt":0.2710925236733202,"score_spread":0.2441869532195018,"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."}}