{"id":"W4392158768","doi":"10.1109/globecom54140.2023.10437667","title":"ERQ: An Efficient Range Query Scheme Under Local Differential Privacy","year":2023,"lang":"en","type":"article","venue":"","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differential privacy; Computer science; Range (aeronautics); Scheme (mathematics); Differential (mechanical device); Range query (database); Theoretical computer science; Information retrieval; Sargable; Data mining; Web search query; Mathematics; Search engine; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001961817,0.0001405918,0.0001330655,0.0001795483,0.0001525892,0.0002009902,0.0009516673,0.00006934774,0.0001511229],"category_scores_gemma":[0.00001045183,0.0001168431,0.00009790211,0.0008645772,0.00008633987,0.0003521362,0.0006669592,0.0001366307,0.0003190142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001682811,"about_ca_system_score_gemma":0.00003754618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008540563,"about_ca_topic_score_gemma":0.00003815919,"domain_scores_codex":[0.9985675,0.00006064727,0.0001717965,0.0004765832,0.000348326,0.000375138],"domain_scores_gemma":[0.9988371,0.00005908533,0.0000302,0.0008617263,0.0000362737,0.0001756564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001504582,0.0003240184,0.0008506087,0.00001696336,0.00002035266,0.00002315695,0.0006721438,0.0002744707,0.0008898343,0.9845844,0.003849031,0.008479972],"study_design_scores_gemma":[0.001249801,0.0001608828,0.08572381,0.00001919306,0.00001037222,0.00001265891,0.0002740855,0.8834423,0.002071326,0.02131829,0.005102803,0.0006144217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3634979,0.00001401864,0.6348483,0.0003142728,0.0003124455,0.00008008805,0.00000669723,0.0005670748,0.000359152],"genre_scores_gemma":[0.9893049,0.000007592062,0.01022044,0.0002935241,0.00008730323,0.000009539646,0.00003765167,0.000007800906,0.00003125857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9632661,"threshold_uncertainty_score":0.4764723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02511657099382504,"score_gpt":0.2678386364916113,"score_spread":0.2427220654977863,"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."}}