{"id":"W3125170070","doi":"10.1109/globecom42002.2020.9322502","title":"Efficient Privacy-Preserving Similarity Range Query based on Pre-Computed Distances in eHealthcare","year":2020,"lang":"en","type":"article","venue":"","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Range query (database); Encryption; Cloud computing; Query optimization; Outsourcing; Web search query; Sargable; Data mining; Web query classification; Information retrieval; Query expansion; Information privacy; Similarity (geometry); Search engine; Computer security; Artificial intelligence","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":[0.0003462558,0.0001899186,0.0002381858,0.0001314085,0.0001208241,0.0001610739,0.001658133,0.00008032489,0.00004208526],"category_scores_gemma":[0.0001524483,0.0001689436,0.00009556436,0.001230388,0.000039681,0.0002025722,0.0006118232,0.0003214056,0.00001057139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003867511,"about_ca_system_score_gemma":0.00008558414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003349496,"about_ca_topic_score_gemma":0.0003785704,"domain_scores_codex":[0.9979491,0.0001996501,0.0003189883,0.0007058109,0.0004429611,0.0003834884],"domain_scores_gemma":[0.9984906,0.0002611464,0.00007586537,0.0008796872,0.00005182797,0.0002408679],"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":[0.001319876,0.003493485,0.3250124,0.002140997,0.00006072341,0.0004820506,0.02626283,0.1836199,0.0003298636,0.3878143,0.0226857,0.04677796],"study_design_scores_gemma":[0.0005685986,0.0001194024,0.08239723,0.00005885313,0.000001508193,3.464794e-7,0.00003326256,0.913765,0.0001265064,0.000996623,0.001729396,0.0002032618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1662154,0.0002284267,0.8161249,0.01483015,0.0002188074,0.000477971,0.00004557136,0.0004490551,0.001409709],"genre_scores_gemma":[0.9628862,0.000004231082,0.03280895,0.004206875,0.00005497702,0.00001405228,0.00001709313,0.000006734967,9.419309e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7966707,"threshold_uncertainty_score":0.6889321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641104833891678,"score_gpt":0.2640169205941572,"score_spread":0.2376058722552404,"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."}}