{"id":"W3191008542","doi":"10.1109/tdsc.2021.3101120","title":"Towards Practical and Privacy-Preserving Multi-Dimensional Range Query Over Cloud","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Cloud computing; Intersection (aeronautics); Predicate (mathematical logic); Point cloud; Data mining; Theoretical computer science; Range query (database); Big data; Information privacy; Server; Computer security; Algorithm; Information retrieval; Computer network; Artificial intelligence; Search engine","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":[0.003394283,0.000702874,0.001758317,0.0009317305,0.00170696,0.003023671,0.002760897,0.001732931,0.001670616],"category_scores_gemma":[0.007575018,0.0004385261,0.001342525,0.002884039,0.001267167,0.008934601,0.006967556,0.002846949,0.0007840004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103944,"about_ca_system_score_gemma":0.002185895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001671617,"about_ca_topic_score_gemma":0.001006,"domain_scores_codex":[0.9922221,0.001696315,0.0007146918,0.001083368,0.003178956,0.001104577],"domain_scores_gemma":[0.9937093,0.001805246,0.0005098658,0.002551102,0.0012076,0.00021684],"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":[0.002010925,0.0006595308,0.00521465,0.0009683922,0.0002991468,0.001940861,0.001769057,0.1514189,0.08240777,0.3503376,0.03040908,0.3725641],"study_design_scores_gemma":[0.0001524049,0.0003052694,0.0007006212,0.00005410358,0.00006865519,0.001964794,0.0006968931,0.8751464,0.02381474,0.08052998,0.01647019,0.00009587094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02076955,0.001377004,0.972179,0.001072309,0.0001232012,0.0002867749,0.0003390177,0.0007315046,0.003121638],"genre_scores_gemma":[0.6949844,0.001966881,0.2972705,0.001037727,0.0003139851,0.0004312627,0.0009297838,0.00009303146,0.002972353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003394283,"threshold_uncertainty_score":0.01795083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02777921603672797,"score_gpt":0.2862058883541309,"score_spread":0.258426672317403,"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."}}