{"id":"W2592139723","doi":"10.1016/j.jbi.2018.03.003","title":"Secure count query on encrypted genomic data","year":2018,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Encryption; Outsourcing; Scalability; Overhead (engineering); Data mining; Trusted third party; Confidentiality; Secure multi-party computation; Data sharing; Information privacy; Computer security; Database; Cryptography","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":[0.001280579,0.0001206449,0.0002257288,0.0003084031,0.0001065734,0.0001717218,0.002691063,0.0001142085,0.00006094099],"category_scores_gemma":[0.0001925844,0.00008502105,0.00007253498,0.0005130938,0.0002746948,0.001478982,0.0006694079,0.000390094,0.0001093589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003834377,"about_ca_system_score_gemma":0.0002518854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003659258,"about_ca_topic_score_gemma":0.000001885652,"domain_scores_codex":[0.9979374,0.00003583849,0.0008911032,0.00009051726,0.000803987,0.0002411209],"domain_scores_gemma":[0.9978744,0.0001351453,0.0005655012,0.0009225243,0.0002080549,0.0002943471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001663257,0.0008927046,0.0002744317,0.0001894702,0.0003190229,0.0001363833,0.0131058,0.000005467643,0.0007205499,0.09634254,0.7130566,0.1747907],"study_design_scores_gemma":[0.001385054,0.001892123,0.001736919,0.0002233486,0.0000370519,0.0006142103,0.0005504765,0.08348096,0.0003071262,0.00971284,0.899726,0.0003338977],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08819508,0.0001224955,0.9062921,0.001572736,0.002073507,0.0000943992,0.0001439326,0.00004852986,0.001457265],"genre_scores_gemma":[0.5969025,0.0004115142,0.3926686,0.007683614,0.002193801,8.739682e-7,0.0001205106,0.00001327721,0.0000052966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5136235,"threshold_uncertainty_score":0.500071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02927523765036445,"score_gpt":0.2858481508442133,"score_spread":0.2565729131938489,"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."}}