{"id":"W3010363409","doi":"10.1109/globecom38437.2019.9013395","title":"EPPS: Efficient Privacy-Preserving Scheme in Distributed Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Homomorphic encryption; Computer science; Cloud computing; Paillier cryptosystem; Encryption; Secure multi-party computation; Differential privacy; Computer security; Scheme (mathematics); Private information retrieval; Adversary model; Adversary; Construct (python library); Distributed computing; Computer network; Cryptography; Data mining; Public-key cryptography","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":[0.004127058,0.0005979081,0.00110192,0.0006260826,0.001322464,0.001643932,0.003016783,0.0018151,0.00205776],"category_scores_gemma":[0.007188725,0.0004329909,0.0008895658,0.001143893,0.001765575,0.00588783,0.006839345,0.002907431,0.0004863296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001514665,"about_ca_system_score_gemma":0.002054057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007075382,"about_ca_topic_score_gemma":0.0005922226,"domain_scores_codex":[0.9950989,0.001697849,0.0003261415,0.0008856886,0.001411701,0.000579769],"domain_scores_gemma":[0.9956433,0.001224979,0.0003953464,0.002149118,0.0003959082,0.0001913954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001806331,0.0003926875,0.002515664,0.0002973289,0.00020813,0.0007370415,0.0008587711,0.2560824,0.02993475,0.452719,0.008916679,0.2455314],"study_design_scores_gemma":[0.0001132289,0.0001532955,0.0002264265,0.00001782274,0.00002872988,0.0002791864,0.0000613152,0.8623978,0.01366065,0.1194583,0.003566302,0.00003682048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01935286,0.0001531783,0.9780739,0.0004567709,0.00003833342,0.0001249504,0.0001124766,0.0006406065,0.001046898],"genre_scores_gemma":[0.8515571,0.0001942236,0.1439563,0.0003295458,0.00005256752,0.0002959691,0.0002328699,0.00006395005,0.003317604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004127058,"threshold_uncertainty_score":0.02182621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604036319692263,"score_gpt":0.2534931698642943,"score_spread":0.2374528066673716,"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."}}