{"id":"W2519626547","doi":"10.3390/s16091463","title":"Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees","year":2016,"lang":"en","type":"article","venue":"Sensors","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"State Key Laboratory of Industrial Control Technology; Zhejiang University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Data aggregator; Computer science; Overhead (engineering); Encryption; Scheme (mathematics); Cloud computing; Information privacy; Outsourcing; Server; Privacy software; Computer security; Secret sharing; Computer network; Wireless sensor network; Cryptography; Data mining","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":[],"consensus_categories":[],"category_scores_codex":[0.00009028891,0.0001625038,0.0001732833,0.00006054267,0.00008699238,0.00003090061,0.0002012603,0.00007450925,0.00007917621],"category_scores_gemma":[0.00004788443,0.0001238432,0.00002190024,0.00007730199,0.0000595132,0.0002166422,0.0001354377,0.0001103575,0.00003856856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007432381,"about_ca_system_score_gemma":0.00002125999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160536,"about_ca_topic_score_gemma":0.00003466858,"domain_scores_codex":[0.9989699,0.00004085852,0.0002264074,0.0003034226,0.0001754954,0.0002839323],"domain_scores_gemma":[0.9991873,0.0001170028,0.00004947114,0.0005337199,0.00002414235,0.000088359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002235429,0.0001654768,0.003806142,0.0003882625,0.0005267072,0.00001187182,0.002587651,0.0453651,0.08552396,0.002262163,0.02010387,0.8390353],"study_design_scores_gemma":[0.005869138,0.0001620094,0.2133936,0.001125259,0.00006939463,0.00005816012,0.0002641759,0.7416946,0.01914302,0.002534407,0.01416237,0.001523824],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9298627,0.0002343234,0.06797158,0.0007027605,0.0005722939,0.0001725194,0.00005563833,0.0003249798,0.000103227],"genre_scores_gemma":[0.9980637,0.0003792422,0.000836623,0.00003647225,0.0003409021,0.000005296728,0.00008385312,0.0000397528,0.0002141665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8375114,"threshold_uncertainty_score":0.5050178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521188591143622,"score_gpt":0.251029997086974,"score_spread":0.2258181111755378,"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."}}