{"id":"W3131045898","doi":"10.1109/tcc.2021.3059026","title":"Enabling Secure and Versatile Packet Inspection With Probable Cause Privacy for Outsourced Middlebox","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Deep packet inspection; Header; Payload (computing); Network packet; Encryption; Cloud computing; Server; Computer security; Computer network; Security token; MD5; Operating system; Hash function","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":[0.002115516,0.0004638435,0.0009418117,0.0005722978,0.0008732761,0.001946559,0.001481861,0.0008627611,0.001531435],"category_scores_gemma":[0.004518132,0.0003999386,0.0006951636,0.0007475516,0.001407532,0.004500648,0.004543663,0.001493188,0.0006054349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009742452,"about_ca_system_score_gemma":0.001378315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007411438,"about_ca_topic_score_gemma":0.000555535,"domain_scores_codex":[0.997454,0.00045238,0.0002328149,0.0003877849,0.0009826797,0.0004903391],"domain_scores_gemma":[0.9927146,0.00127488,0.0006756203,0.004650421,0.0004654141,0.0002190548],"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":[0.003069549,0.0005604331,0.01573615,0.000576989,0.0002746476,0.003499204,0.002590416,0.08423544,0.2189814,0.3312857,0.009891135,0.3292989],"study_design_scores_gemma":[0.0001487381,0.0003364273,0.002528104,0.00006706586,0.0001455466,0.001728228,0.0005885953,0.7390675,0.1524315,0.09115763,0.01169231,0.0001083343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1464373,0.0003521482,0.8458139,0.000537962,0.00006210803,0.0002243708,0.0002218865,0.00196349,0.004386779],"genre_scores_gemma":[0.9606779,0.0001615234,0.03701796,0.0001118125,0.00003077728,0.00005827261,0.0001347297,0.00004815734,0.001758866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002115516,"threshold_uncertainty_score":0.01118809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410545330437153,"score_gpt":0.2460714705639933,"score_spread":0.2219660172596217,"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."}}