{"id":"W4404283960","doi":"10.36227/techrxiv.173030575.54999087/v2","title":"CryptoKANs: Enhancing Privacy-Preserving Machine Learning in IoT Environments using Kolmogorov-Arnold Networks over Encrypted Data","year":2024,"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":"","funders":"Mitacs","keywords":"Encryption; Computer science; Internet of Things; Computer network; Artificial intelligence; Human–computer interaction; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.002279766,0.0009758089,0.0008826684,0.0008465163,0.0002320614,0.00119764,0.007346347,0.0006587174,0.0003986653],"category_scores_gemma":[0.0004848934,0.001022918,0.0002145515,0.0009648163,0.00008973279,0.001054854,0.052061,0.004422959,0.000121054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053939,"about_ca_system_score_gemma":0.0004027771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003513382,"about_ca_topic_score_gemma":0.0004476609,"domain_scores_codex":[0.9924643,0.0005873821,0.001390558,0.003182838,0.001195687,0.001179213],"domain_scores_gemma":[0.9937952,0.0004432783,0.000570403,0.00488495,0.00004245079,0.0002637775],"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":[0.00003445934,0.0001998503,0.00234853,0.0005289052,0.0001879535,0.0003696279,0.0009069529,0.929901,0.05203039,0.0006969236,0.0005662898,0.01222909],"study_design_scores_gemma":[0.0004998207,0.00003873337,0.0009304941,0.001509103,0.00006585855,0.00002160167,0.00002545623,0.9885459,0.002272798,0.00373811,0.00134768,0.001004455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05056868,0.003214332,0.9419599,0.000370255,0.001595743,0.0008361597,0.00002868808,0.000637598,0.0007886037],"genre_scores_gemma":[0.8137189,0.0004421966,0.1831914,0.0004800579,0.0006659019,0.00005380548,0.0005115506,0.0002072155,0.0007289226],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7631502,"threshold_uncertainty_score":0.9998392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04248047461902808,"score_gpt":0.2896212428502208,"score_spread":0.2471407682311927,"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."}}