{"id":"W3106624205","doi":"","title":"Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release","year":2019,"lang":"en","type":"preprint","venue":"eScholarship@McGill (McGill)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Context (archaeology); Information privacy; Smart meter; Information sensitivity; Mutual information; Set (abstract data type); Data mining; Adversary; Private information retrieval; Big data; Measure (data warehouse); Computer security; Artificial intelligence; Smart grid; Engineering","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":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","open_science","research_integrity"],"category_scores_codex":[0.004051994,0.001407562,0.001392193,0.001248189,0.00150189,0.001443842,0.1114983,0.001660947,0.00008973655],"category_scores_gemma":[0.1734805,0.001531931,0.000497551,0.001330892,0.0001719958,0.008754178,0.3850457,0.004986088,0.0005048399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113178,"about_ca_system_score_gemma":0.000372293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004597889,"about_ca_topic_score_gemma":0.0001243205,"domain_scores_codex":[0.9903578,0.0008315184,0.001976417,0.003328633,0.001703879,0.00180181],"domain_scores_gemma":[0.9517648,0.002341427,0.001780584,0.04261595,0.001051569,0.0004456665],"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":[0.000558547,0.0008326561,0.001738662,0.006527771,0.001883472,0.0001402903,0.00003421646,0.0357885,0.003621861,0.02372447,0.01428796,0.9108616],"study_design_scores_gemma":[0.001301442,0.0001220919,0.000324361,0.000623546,0.0001313151,0.00001261596,0.00001313104,0.7256453,0.006621328,0.1059318,0.1576399,0.001633267],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3001839,0.002885855,0.460564,0.02305817,0.0295568,0.03201855,0.0356301,0.07149176,0.04461094],"genre_scores_gemma":[0.3649722,0.0001338342,0.6269244,0.000988372,0.00006229108,0.0006142693,0.005918417,0.0002103169,0.0001758896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9092283,"threshold_uncertainty_score":0.9998674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04336440305178583,"score_gpt":0.2692250425781557,"score_spread":0.2258606395263699,"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."}}