{"id":"W3122242314","doi":"10.36227/techrxiv.13650059.v1","title":"SecureDL: A privacy preserving deep learning model for image recognition over cloud","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Encryption; Cloud computing; Homomorphic encryption; Block (permutation group theory); Cryptography; Server; Flexibility (engineering); Secure multi-party computation; Computer security; Information privacy; Artificial intelligence; Theoretical computer science; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007611448,0.0003597977,0.0005171426,0.0002924984,0.0002687282,0.0008610455,0.001324537,0.0006298591,0.00145589],"category_scores_gemma":[0.001301518,0.0002363527,0.0005482761,0.0003904445,0.0006945739,0.001644785,0.001490373,0.001414586,0.0005207073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072631,"about_ca_system_score_gemma":0.001390766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003685819,"about_ca_topic_score_gemma":0.003437677,"domain_scores_codex":[0.9995254,0.00009030526,0.00002854259,0.00008917473,0.0001849374,0.00008163888],"domain_scores_gemma":[0.9995874,0.00009819498,0.00006958652,0.0001345931,0.00007856735,0.00003168988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000489506,0.0002436146,0.001661866,0.0001054082,0.00008018845,0.0002356346,0.00008861928,0.7565883,0.01747015,0.06718935,0.00470802,0.1511394],"study_design_scores_gemma":[0.000005621287,0.00001849627,0.00005034909,0.00000188421,0.000002528189,0.00001611298,0.000002727061,0.9933344,0.001606664,0.004523897,0.0004337514,0.000003525227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01939789,0.0001619565,0.9780775,0.0002897911,0.0000269335,0.00004185769,0.0001466772,0.0006985948,0.001158825],"genre_scores_gemma":[0.8329539,0.0003937014,0.158552,0.0002866538,0.0000473655,0.0001439488,0.0005174165,0.0001191204,0.006985862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003685819,"threshold_uncertainty_score":0.007782519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03991579275938122,"score_gpt":0.2847516694165371,"score_spread":0.2448358766571559,"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."}}