{"id":"W4378533280","doi":"10.1007/s11227-023-05378-x","title":"Local differentially private federated learning with homomorphic encryption","year":2023,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Homomorphic encryption; Computer science; Differential privacy; Shuffling; MNIST database; Inference; Encryption; Reduction (mathematics); Federated learning; Data mining; Machine learning; Artificial intelligence; Theoretical computer science; Computer security; Deep learning","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001936598,0.0001647014,0.0002405127,0.0002485276,0.0004970119,0.0002596345,0.01060473,0.00007434759,0.000005683754],"category_scores_gemma":[0.002269601,0.0001006775,0.00005582286,0.001101829,0.0001487174,0.0006703485,0.01574372,0.0008828319,0.00004483859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000714061,"about_ca_system_score_gemma":0.00007817319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001886633,"about_ca_topic_score_gemma":0.000004572308,"domain_scores_codex":[0.9981326,0.0002935884,0.0004332727,0.0001884037,0.0005511008,0.000400985],"domain_scores_gemma":[0.9977019,0.0004431546,0.0002903623,0.001318931,0.0001843936,0.00006127479],"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":[0.0003697723,0.0003230237,0.0252403,0.0002223961,0.0008727091,0.001625696,0.005812512,0.1145479,0.1534037,0.005469959,0.03884384,0.6532682],"study_design_scores_gemma":[0.0006684234,0.0004356711,0.01026068,0.0003304077,0.00003041679,0.0009534056,0.000548006,0.9627783,0.007987841,0.01545117,0.000307697,0.0002479712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4905981,0.00004134428,0.5043477,0.004369326,0.0001445775,0.00004489127,2.144616e-7,0.0004338151,0.00002011236],"genre_scores_gemma":[0.9634251,0.00007891872,0.03632979,0.00005204071,0.00008195704,5.475426e-7,0.000001659202,0.0000213828,0.000008608551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8482304,"threshold_uncertainty_score":0.9947484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252148229056052,"score_gpt":0.2452658358440073,"score_spread":0.2200510129384021,"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."}}