{"id":"W3131894619","doi":"10.48550/arxiv.2102.09651","title":"Obfuscated Access and Search Patterns in Searchable Encryption","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Encryption; Overhead (engineering); Obfuscation; Scheme (mathematics); Cloud computing; Symmetric-key algorithm; Web search query; Database; Outsourcing; Computer network; Computer security; Search engine; Public-key cryptography; Information retrieval; 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":[0.0037985,0.0008145366,0.001487204,0.00121956,0.0008820234,0.001408747,0.001552241,0.001540387,0.001262777],"category_scores_gemma":[0.01129907,0.0006862231,0.001325298,0.002024645,0.003081559,0.008675199,0.003901101,0.002454133,0.0005098877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386886,"about_ca_system_score_gemma":0.001296399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007418632,"about_ca_topic_score_gemma":0.0004537652,"domain_scores_codex":[0.9934292,0.001991008,0.0008255258,0.0006647555,0.001920271,0.001169198],"domain_scores_gemma":[0.9840681,0.005511641,0.001702596,0.007930921,0.0005663697,0.0002202471],"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":[0.003958889,0.0007024362,0.01137255,0.001128095,0.0003656152,0.001714543,0.002112367,0.1807497,0.07184862,0.379581,0.004818672,0.3416474],"study_design_scores_gemma":[0.0003823209,0.001098499,0.003498814,0.0002905503,0.0002613601,0.003425725,0.0004074361,0.583796,0.108112,0.2842206,0.01429729,0.0002094415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3302824,0.003662938,0.6564896,0.001284472,0.0001078839,0.00029346,0.0004468317,0.001920877,0.005511521],"genre_scores_gemma":[0.9508052,0.0005747767,0.04657383,0.0001296504,0.00004084181,0.0000991037,0.0001327975,0.00004785948,0.00159591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0037985,"threshold_uncertainty_score":0.02008861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1097151670440809,"score_gpt":0.2230054270413309,"score_spread":0.1132902599972501,"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."}}