{"id":"W4297687590","doi":"10.48550/arxiv.1707.08919","title":"A Two-Stage Architecture for Differentially Private Kalman Filtering and\\n LQG Control","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Linear-quadratic-Gaussian control; Differential privacy; Kalman filter; Computer science; Linear-quadratic regulator; Architecture; Gaussian; Control theory (sociology); Control (management); Algorithm; Control engineering; Artificial intelligence; Engineering","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":[0.001627195,0.0008794667,0.0008712852,0.0004503868,0.0008828801,0.001890222,0.003071901,0.002260162,0.003288952],"category_scores_gemma":[0.00253166,0.0006640298,0.0008772542,0.0005705857,0.001573095,0.001981338,0.002609526,0.002390366,0.001143291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001330649,"about_ca_system_score_gemma":0.00217934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004425995,"about_ca_topic_score_gemma":0.004253705,"domain_scores_codex":[0.9978433,0.0004417444,0.0001183276,0.000655573,0.0006905397,0.0002505039],"domain_scores_gemma":[0.9990045,0.0002692372,0.00008553677,0.0003134889,0.0002658205,0.00006147542],"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":[0.000741346,0.0003449642,0.001695839,0.0002335665,0.0001666426,0.0003602873,0.0006658292,0.5369129,0.05631238,0.168774,0.002895936,0.2308963],"study_design_scores_gemma":[0.00003996409,0.0001559945,0.0001760795,0.00001325828,0.00002338005,0.00006044414,0.00001465035,0.9732012,0.006931726,0.01652028,0.002834488,0.00002855876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004836346,0.00008285316,0.9925991,0.0001228362,0.00003472709,0.00007103234,0.00001854565,0.0004606082,0.001773982],"genre_scores_gemma":[0.6072578,0.0002294288,0.3812699,0.0002896126,0.0001446154,0.0004210912,0.0001760196,0.00007702202,0.01013446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004425995,"threshold_uncertainty_score":0.01100266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06955413486759925,"score_gpt":0.2226990193972865,"score_spread":0.1531448845296873,"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."}}