{"id":"W2990372367","doi":"10.1109/access.2019.2954043","title":"Achieving Efficient and Privacy-Preserving Multi-Keyword Conjunctive Query Over Cloud","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; New Brunswick Innovation Foundation","keywords":"Computer science; Cloud computing; Encryption; Query optimization; Web search query; Homomorphic encryption; Query expansion; Sargable; Web query classification; Range query (database); Keyword search; Scheme (mathematics); Data mining; Information retrieval; Database; Computer security; Search engine","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":[0.000330714,0.0001822667,0.0002118875,0.0001413497,0.0001525379,0.000616728,0.001947567,0.00007667765,0.00004153746],"category_scores_gemma":[0.00006207738,0.0001643905,0.00007095019,0.0004637014,0.00005960482,0.001361877,0.001880956,0.0002293567,0.00003452938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002277117,"about_ca_system_score_gemma":0.00003434292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002889147,"about_ca_topic_score_gemma":0.00004036621,"domain_scores_codex":[0.9984789,0.00008454281,0.000209939,0.0006095873,0.0002752974,0.000341753],"domain_scores_gemma":[0.9984465,0.0002215578,0.0001154048,0.001029498,0.00006517689,0.0001219072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002168419,0.001149765,0.8138572,0.0006034278,0.0002728732,0.00009672867,0.01143461,0.00263245,0.01829495,0.1145488,0.008973848,0.02791854],"study_design_scores_gemma":[0.003281305,0.0001636669,0.7213414,0.0003168425,0.00003535638,0.00002592945,0.0001516866,0.2518121,0.006897785,0.004878949,0.009826533,0.001268511],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7632464,0.0002303524,0.2342207,0.0001133258,0.001226513,0.0002539369,0.00001088159,0.0001142196,0.0005836565],"genre_scores_gemma":[0.9938864,0.00002653588,0.005533376,0.0004079855,0.0001075088,0.000009542487,0.000003285765,0.00001033587,0.00001499201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2491796,"threshold_uncertainty_score":0.670365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232121973034711,"score_gpt":0.2899361421350249,"score_spread":0.2676149224046778,"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."}}