{"id":"W4312102382","doi":"10.48786/edbt.2023.44","title":"Frequency Estimation of Evolving Data Under Local Differential Privacy","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; European Commission","keywords":"Differential privacy; Computer science; Hash function; Protocol (science); Context (archaeology); Computer security; Domain (mathematical analysis); Information leakage; Information privacy; Data collection; Data mining; Statistics; Mathematics","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":[0.002604561,0.000431874,0.001012001,0.001004549,0.0003359367,0.001260411,0.001190093,0.001061984,0.0008095233],"category_scores_gemma":[0.02212442,0.0004374674,0.0005517345,0.001632348,0.0009741556,0.002070754,0.001747694,0.001224229,0.0002606296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008806825,"about_ca_system_score_gemma":0.0005718864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001560223,"about_ca_topic_score_gemma":0.001173385,"domain_scores_codex":[0.9983565,0.0005000856,0.0001018357,0.0003918173,0.0004800568,0.0001695976],"domain_scores_gemma":[0.9874012,0.009558129,0.0008739766,0.001411669,0.0005588075,0.0001963241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005673129,0.00008237866,0.007974499,0.0001877348,0.0001046445,0.0003199732,0.0002912508,0.7507599,0.01457136,0.06307606,0.001287251,0.1607777],"study_design_scores_gemma":[0.000006111732,0.00002012765,0.0006482161,0.000005111801,0.000005985792,0.0000606699,0.00001356104,0.9858337,0.001580119,0.01162047,0.0001996805,0.000006161253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0687552,0.0001352049,0.9301407,0.000269759,0.0000231374,0.00001381355,0.0001248382,0.0001193361,0.0004179847],"genre_scores_gemma":[0.9129868,0.0003879903,0.08356304,0.0001071933,0.0001472246,0.0000539122,0.0005117272,0.00005931813,0.002182856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002604561,"threshold_uncertainty_score":0.01377439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05308594904390154,"score_gpt":0.2837901192056557,"score_spread":0.2307041701617541,"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."}}