{"id":"W3212030079","doi":"","title":"Iron: Functional Encryption using Intel SGX","year":2016,"lang":"en","type":"preprint","venue":"IACR Cryptology ePrint Archive","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Guard (computer science); Cryptographic primitive; Cryptography; Encryption; Context (archaeology); Theoretical computer science; Functional encryption; Construct (python library); Cryptographic protocol; Computer security; Programming language; Ciphertext","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":[0.0009877115,0.0006080023,0.0003650408,0.0004575852,0.0004571239,0.001062657,0.0009839615,0.0006928213,0.007541839],"category_scores_gemma":[0.001858993,0.0003535266,0.0004981613,0.0002400189,0.001295827,0.002801433,0.001547174,0.001256008,0.002286154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008933861,"about_ca_system_score_gemma":0.0007952209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008824008,"about_ca_topic_score_gemma":0.00099712,"domain_scores_codex":[0.9992649,0.000143565,0.00005323986,0.0001064836,0.0002955163,0.0001362742],"domain_scores_gemma":[0.9993818,0.0001254697,0.00005853865,0.0002984035,0.0001038901,0.00003189908],"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":[0.001177718,0.0001979227,0.002180741,0.0004052609,0.0000454917,0.0004409308,0.000586246,0.01662734,0.03642327,0.7044913,0.05405114,0.1833726],"study_design_scores_gemma":[0.0003279437,0.0009690332,0.001841716,0.0002688218,0.0001036345,0.001369393,0.0001360546,0.2437806,0.1481633,0.2630764,0.3398428,0.0001202419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05975805,0.0006817808,0.8336356,0.0009364483,0.0002395668,0.0003262794,0.0008073024,0.03588612,0.0677289],"genre_scores_gemma":[0.7833315,0.0003505206,0.184037,0.0005658993,0.00007798117,0.000218368,0.00142061,0.001215613,0.02878263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007541839,"threshold_uncertainty_score":0.02522993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05872375147886557,"score_gpt":0.2922273820116055,"score_spread":0.2335036305327399,"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."}}