{"id":"W2554001956","doi":"10.1109/mcc.2016.107","title":"Privacy-Preserving Access to Big Data in the Cloud","year":2016,"lang":"en","type":"article","venue":"IEEE Cloud Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Japan Society for the Promotion of Science","keywords":"Cloud computing; Computer science; Cloud service provider; Computer security; Data access; Service provider; Heuristic; Cloud storage; Big data; Cloud computing security; Information privacy; Data security; Load balancing (electrical power); Database; Service (business); Distributed computing; Encryption; Operating system","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":[0.00299271,0.0005834192,0.001025402,0.0008481701,0.001545565,0.003619154,0.001709594,0.001529877,0.002032368],"category_scores_gemma":[0.008976703,0.0005263459,0.000834122,0.002561079,0.002378548,0.008909025,0.004264264,0.002410045,0.0009990261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089099,"about_ca_system_score_gemma":0.001912987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000521964,"about_ca_topic_score_gemma":0.0006466413,"domain_scores_codex":[0.995754,0.00126424,0.0002663434,0.0006105429,0.001528903,0.0005759786],"domain_scores_gemma":[0.9898538,0.003849962,0.000632458,0.004904725,0.0005013567,0.0002576094],"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":[0.000927111,0.0002546567,0.003888008,0.0009083466,0.0001895882,0.0007884795,0.001030585,0.06261138,0.02679782,0.5912735,0.02191665,0.289414],"study_design_scores_gemma":[0.00007473589,0.0001530433,0.001141732,0.0001532846,0.00006662811,0.001245071,0.0004920224,0.3231916,0.03003515,0.5999703,0.04341396,0.00006249034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04612056,0.004010045,0.921968,0.006105458,0.000286545,0.0001842463,0.0006032552,0.001238437,0.01948347],"genre_scores_gemma":[0.8898572,0.004643717,0.0967471,0.001142236,0.0006926153,0.0001984317,0.0005263411,0.0001594757,0.006032981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003619154,"threshold_uncertainty_score":0.01582712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043936586537758,"score_gpt":0.3256295497266825,"score_spread":0.2212358910729067,"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."}}