{"id":"W4313025490","doi":"10.1109/tdsc.2022.3211870","title":"PRkNN: Efficient and Privacy-Preserving Reverse kNN Query Over Encrypted Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encryption; Cloud computing; Query optimization; Information privacy; Data mining; Information retrieval; Computer security","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007344755,0.0002299148,0.000239641,0.0002509983,0.001516455,0.0002808714,0.001358762,0.00006330747,0.00009106056],"category_scores_gemma":[0.00001448443,0.0002425176,0.00006151009,0.0007048546,0.00005963737,0.0005635883,0.0003723233,0.0006469829,0.000002989277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003578285,"about_ca_system_score_gemma":0.00006877515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002643823,"about_ca_topic_score_gemma":0.00005554797,"domain_scores_codex":[0.9977221,0.0002022853,0.0003106821,0.0009253384,0.0004332218,0.0004063548],"domain_scores_gemma":[0.9980629,0.0002872858,0.0001113202,0.001336159,0.00003736805,0.0001649403],"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":[0.0008399311,0.00554581,0.00270479,0.001426901,0.001238654,0.001391186,0.04994909,0.3111557,0.009641166,0.07002039,0.03087278,0.5152136],"study_design_scores_gemma":[0.0008228939,0.0001379105,0.0003450465,0.00005217385,0.00003682547,0.0001866797,0.0003690355,0.9844667,0.0004825995,0.000684903,0.01201757,0.0003976279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2026028,0.0003812572,0.7954225,0.0003028305,0.0005700665,0.0002356113,0.0001684767,0.0001900857,0.0001264166],"genre_scores_gemma":[0.9833099,0.000062229,0.01618028,0.0003433134,0.00004271375,0.000008168101,0.00002599447,0.00001504999,0.00001232936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7807071,"threshold_uncertainty_score":0.9997835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373500829044834,"score_gpt":0.2527698077873641,"score_spread":0.2290347994969158,"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."}}