{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005145191,0.0008224314,0.001152257,0.0005837434,0.0004764632,0.001295053,0.002319308,0.001615658,0.0009510674],"category_scores_gemma":[0.01328712,0.0006240547,0.0007993649,0.001001935,0.002153062,0.003528052,0.002960268,0.003575184,0.0003271234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002031841,"about_ca_system_score_gemma":0.001515535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001820739,"about_ca_topic_score_gemma":0.001899517,"domain_scores_codex":[0.9971999,0.001246332,0.0001351929,0.0005355533,0.0006318926,0.0002511542],"domain_scores_gemma":[0.9916595,0.005466457,0.0008387358,0.001411674,0.0004380621,0.0001855825],"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":[0.0003454507,0.0001834012,0.001802161,0.000125616,0.0001129524,0.0001602802,0.0002384439,0.8355326,0.003421334,0.05340804,0.00228148,0.1023883],"study_design_scores_gemma":[0.00000640517,0.00002890674,0.000099078,0.000006512189,0.00000634702,0.00001734237,0.000006869549,0.9810928,0.001333706,0.01708323,0.0003138634,0.000004999186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02487648,0.0003502765,0.9725869,0.0005957208,0.00002337046,0.00003214235,0.0001148024,0.0004805979,0.0009396413],"genre_scores_gemma":[0.8796062,0.0004174381,0.1154208,0.0004785467,0.00007567623,0.0001269925,0.00051175,0.0001240087,0.003238372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005145191,"threshold_uncertainty_score":0.02721071,"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."}}