{"id":"W4414539178","doi":"10.1109/icc52391.2025.11161185","title":"An Efficient Private Set Frequency Query Scheme Under Local Differential Privacy","year":2025,"lang":"en","type":"article","venue":"","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Queen's University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Differential privacy; Set (abstract data type); Scheme (mathematics); Bloom filter; Crowdsourcing; Query optimization; Filter (signal processing); Information privacy; Query expansion","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002772328,0.0006581038,0.001745028,0.001280634,0.001842523,0.001660492,0.00283813,0.001795522,0.002226707],"category_scores_gemma":[0.007839112,0.0003798838,0.0008333526,0.002735457,0.00125788,0.005155877,0.005354487,0.001577609,0.0007989138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002100849,"about_ca_system_score_gemma":0.002288534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001684145,"about_ca_topic_score_gemma":0.001156255,"domain_scores_codex":[0.9940309,0.001270176,0.0003796222,0.0009879505,0.002599931,0.0007313865],"domain_scores_gemma":[0.9945331,0.001735023,0.0005527731,0.002019959,0.0008985717,0.0002607134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003002921,0.0006074354,0.00463172,0.0005735062,0.000235175,0.0008878827,0.001740068,0.1231463,0.1106763,0.2779497,0.0155437,0.4610053],"study_design_scores_gemma":[0.0003717208,0.0004657686,0.001170866,0.00003628532,0.00009779423,0.001236201,0.0003173341,0.8387352,0.03145578,0.1107812,0.01514648,0.0001854389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03466885,0.0003859822,0.9586626,0.0005964457,0.00008366915,0.0003632331,0.0004269896,0.0008997091,0.003912523],"genre_scores_gemma":[0.814796,0.000308121,0.1777841,0.0004207938,0.0001621921,0.0004326471,0.0005141046,0.00005574641,0.005526206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00283813,"threshold_uncertainty_score":0.01524282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255950218555766,"score_gpt":0.2705958176043476,"score_spread":0.2580363154187899,"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."}}