{"id":"W2789789243","doi":"10.3233/jcs-171103","title":"HardIDX: Practical and secure index with SGX in a malicious environment","year":2018,"lang":"en","type":"article","venue":"Journal of Computer Security","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Encryption; Memory footprint; Software; Code (set theory); Index (typography); Information leakage; Computer security; Operating system; Embedded system; World Wide Web; Set (abstract data type)","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":[0.001024344,0.0005688786,0.0006235199,0.0006805112,0.0008366952,0.002216707,0.001775588,0.0008849422,0.004203958],"category_scores_gemma":[0.003756616,0.0004537269,0.0004990192,0.0005735048,0.001555948,0.005088356,0.0041518,0.00127375,0.001949797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099307,"about_ca_system_score_gemma":0.001482913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001010756,"about_ca_topic_score_gemma":0.000876372,"domain_scores_codex":[0.998104,0.0001983231,0.0001984908,0.0002089584,0.001011391,0.0002788431],"domain_scores_gemma":[0.9975084,0.0004570662,0.0002207594,0.001410147,0.0002695842,0.0001340961],"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":[0.003904877,0.0006587646,0.01208094,0.001300087,0.0002329775,0.002188757,0.001972694,0.03899546,0.1753454,0.3291838,0.0459175,0.3882187],"study_design_scores_gemma":[0.0004499816,0.001164392,0.00274225,0.0001990554,0.0001196301,0.003026095,0.0004143069,0.566916,0.2486414,0.08689693,0.0892667,0.0001633112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2061528,0.002297232,0.7279772,0.001058646,0.0003157367,0.000685379,0.0006950908,0.03199355,0.02882444],"genre_scores_gemma":[0.8277217,0.0004407178,0.1578699,0.0002626212,0.00005757459,0.0001900896,0.0006715324,0.000838579,0.01194734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004203958,"threshold_uncertainty_score":0.0140636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007252090349492954,"score_gpt":0.2338831207959884,"score_spread":0.2266310304464954,"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."}}