{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007341249,0.0004485177,0.0004369134,0.0002267164,0.0002089249,0.001240773,0.00148711,0.0003557864,0.000114292],"category_scores_gemma":[0.0005859899,0.0004920593,0.0003975049,0.0002689978,0.00003459764,0.001343152,0.003834435,0.001058708,0.00003362324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002762288,"about_ca_system_score_gemma":0.0003440157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464878,"about_ca_topic_score_gemma":0.00005923086,"domain_scores_codex":[0.9967937,0.000169103,0.0005507949,0.001397,0.0005491084,0.0005403347],"domain_scores_gemma":[0.9974462,0.0002284593,0.000391217,0.001141365,0.0006306749,0.000162087],"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":[0.0001489067,0.000589595,0.00003921456,0.002427754,0.0003181558,0.0001164346,0.01456944,0.8138692,0.05655468,0.004182344,0.00440356,0.1027807],"study_design_scores_gemma":[0.0006952065,0.00004800242,0.00003413762,0.0002887413,0.00003937502,0.0000121175,0.0000553894,0.9457465,0.006331624,0.04601304,0.0001631412,0.0005727698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02620698,0.0001781945,0.9694262,0.0006023492,0.0006919462,0.0009446494,0.00001409305,0.000700941,0.001234679],"genre_scores_gemma":[0.1337676,0.00009075202,0.863605,0.000469393,0.0004541021,0.0003898221,0.0004292605,0.00007816393,0.0007159331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1318772,"threshold_uncertainty_score":0.999796,"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."}}