{"id":"W7130312948","doi":"10.1109/tencon66050.2025.11374984","title":"ActivityNet-HE: An Encryption-Enabled Deep Learning-Based Framework for Secure Human Activity Monitoring","year":2025,"lang":"","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Homomorphic encryption; Activity recognition; Encryption; Inference; Principal component analysis; Information privacy; Raw data; Artificial neural network; Curse of dimensionality","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005247629,0.0004937662,0.0004206466,0.0003014179,0.000184964,0.0004328769,0.001039513,0.0005717218,0.00139527],"category_scores_gemma":[0.0010088,0.0002297874,0.0003846755,0.000252012,0.0004579803,0.001145209,0.001099168,0.001218198,0.0004340258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005881879,"about_ca_system_score_gemma":0.0007943583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003107875,"about_ca_topic_score_gemma":0.006662036,"domain_scores_codex":[0.9997265,0.00006300758,0.00001403676,0.00006852506,0.00008013393,0.00004788667],"domain_scores_gemma":[0.9997824,0.00006238549,0.0000321337,0.00006463019,0.00004026623,0.00001818323],"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":[0.0005508845,0.000352589,0.003351456,0.0001658689,0.000162927,0.0002595422,0.0001018234,0.4812607,0.02296576,0.02916409,0.009709288,0.4519551],"study_design_scores_gemma":[0.000009859924,0.00005381962,0.0003687232,0.000008337921,0.000009349639,0.00005541747,0.000007744665,0.9830036,0.00683148,0.007591847,0.002051786,0.000008117624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02167763,0.0004982831,0.9726376,0.000239808,0.00005499254,0.0000566148,0.0003522187,0.002806597,0.001676279],"genre_scores_gemma":[0.693759,0.0006975167,0.296004,0.0003472796,0.00005266561,0.000156077,0.001329483,0.0001719255,0.007482067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003107875,"threshold_uncertainty_score":0.006179571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03862477731252369,"score_gpt":0.3385003174323407,"score_spread":0.299875540119817,"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."}}