{"id":"W1590453572","doi":"10.1007/978-3-642-40084-1_30","title":"How to Run Turing Machines on Encrypted Data","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Encryption; Homomorphic encryption; Cryptography; Theoretical computer science; Turing machine; Functional encryption; Algorithm; Computer security; Ciphertext; Computation","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007861244,0.0006894683,0.0005619882,0.001264414,0.0003388713,0.002285989,0.01188782,0.0002967953,0.00003881469],"category_scores_gemma":[0.0002152574,0.0005882962,0.0001134305,0.001004428,0.0004010751,0.001837917,0.006718883,0.001000298,0.0001592192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152942,"about_ca_system_score_gemma":0.0002337727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000655767,"about_ca_topic_score_gemma":0.0001531029,"domain_scores_codex":[0.9946172,0.00004026083,0.0004085022,0.002843343,0.001263659,0.0008270903],"domain_scores_gemma":[0.9932101,0.0004899913,0.0002188299,0.005526122,0.0001839035,0.0003710902],"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":[0.000007304899,0.00004421931,0.00003171041,0.00003772374,0.00001510896,0.0000772995,0.000453426,0.001700324,0.0001533075,0.09864315,0.0007868673,0.8980496],"study_design_scores_gemma":[0.0004932536,0.0005268982,0.000617491,0.000935901,0.00001730221,0.00009810059,3.422224e-7,0.5622832,0.001413741,0.3716881,0.05965313,0.00227258],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001320073,0.0002693424,0.9906398,0.003363685,0.002255919,0.000560939,0.00008103093,0.0002631016,0.002434172],"genre_scores_gemma":[0.05475781,0.00005586899,0.9385161,0.005056913,0.00125703,0.00001697757,0.00009469854,0.00005308126,0.0001915607],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.895777,"threshold_uncertainty_score":0.9996569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02802479393924267,"score_gpt":0.251666548258778,"score_spread":0.2236417543195353,"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."}}