{"id":"W4362653996","doi":"10.1109/tsc.2023.3264710","title":"SecBerg: Secure and Practical Iceberg Queries in Cloud","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Secret sharing; Encryption; Overhead (engineering); Materialized view; Database; Homomorphic encryption; Analytics; Cryptography; View; Computer security; Operating system; Database design","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":[0.0004043384,0.0001813687,0.0001908294,0.0003387128,0.0003096292,0.0002434839,0.0003102018,0.0001034065,0.000009032834],"category_scores_gemma":[0.000002818806,0.0001873521,0.0000638323,0.001605873,0.0000479283,0.0007618184,0.00002199611,0.0004136964,0.00005094167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001889191,"about_ca_system_score_gemma":0.00003414312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002323215,"about_ca_topic_score_gemma":0.00123685,"domain_scores_codex":[0.9984816,0.000119474,0.0002736422,0.0005029243,0.0002516896,0.0003707245],"domain_scores_gemma":[0.9989413,0.0004280991,0.00007030504,0.0004102176,0.0000398439,0.0001102101],"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":[0.0005783753,0.002974989,0.009462337,0.003140731,0.0006097019,0.001935611,0.1545813,0.1263127,0.003891141,0.3411367,0.001881004,0.3534954],"study_design_scores_gemma":[0.001166414,0.0001986649,0.007299071,0.0002930848,0.0000252529,0.0001674379,0.002418633,0.9724875,0.002150147,0.0076764,0.005405948,0.0007114396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3456737,0.00002969895,0.6517971,0.001041347,0.0007067611,0.0001173119,0.00001310246,0.0004501071,0.0001708749],"genre_scores_gemma":[0.9866328,0.00006390813,0.01272557,0.0004830571,0.00006655662,0.000006664664,0.000004791787,0.0000113134,0.000005371773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8461748,"threshold_uncertainty_score":0.7639996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01741751187869095,"score_gpt":0.2780257320390476,"score_spread":0.2606082201603567,"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."}}