{"id":"W3216597029","doi":"10.1016/j.inffus.2021.11.013","title":"PrivStream: A privacy-preserving inference framework on IoT streaming data at the edge","year":2021,"lang":"en","type":"article","venue":"Information Fusion","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Differential privacy; Edge computing; Server; Robustness (evolution); Enhanced Data Rates for GSM Evolution; Edge device; Noise (video); Inference; Computation; Distributed computing; Computer network; Data mining; Artificial intelligence; Algorithm; Cloud computing; Operating system","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","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006510047,0.0002297254,0.0001767892,0.0001533915,0.0005959484,0.0006974832,0.03627973,0.0002207101,0.0001884933],"category_scores_gemma":[0.07586186,0.0001744742,0.00004444122,0.0009872228,0.00008436558,0.003248981,0.2678737,0.000574443,0.0005894855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001679296,"about_ca_system_score_gemma":0.0001855029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004050533,"about_ca_topic_score_gemma":0.0000233699,"domain_scores_codex":[0.9977507,0.0001103054,0.0004708389,0.0004875249,0.0007692627,0.000411339],"domain_scores_gemma":[0.9756201,0.00114584,0.0003395316,0.02261907,0.0002101891,0.00006529246],"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":[0.00002130281,0.0001144544,0.002283053,0.00009737719,0.00003502546,0.0000236918,0.00152727,0.0003160405,0.0005980323,0.01947519,0.3707414,0.6047671],"study_design_scores_gemma":[0.0004215585,0.00008162292,0.00865622,0.0008117553,0.00001513329,0.00005697025,0.0003595094,0.6844884,0.01609565,0.09405337,0.1943433,0.0006165033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1895397,0.0002259076,0.7166096,0.07892374,0.0014058,0.000613258,0.0001999134,0.001698355,0.01078374],"genre_scores_gemma":[0.5515751,0.0004142946,0.4436283,0.002839119,0.0001581955,0.0000558513,0.001036596,0.00002497329,0.0002675437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6841724,"threshold_uncertainty_score":0.9689345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05031979247492524,"score_gpt":0.3063239898273997,"score_spread":0.2560041973524745,"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."}}