{"id":"W2782565858","doi":"10.1109/glocom.2017.8253982","title":"Achieving Privacy-Preserving Multi Dot-Product Query in Fog Computing-Enhanced IoT","year":2017,"lang":"en","type":"article","venue":"","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Homomorphic encryption; Scheme (mathematics); Enhanced Data Rates for GSM Evolution; Edge computing; Encryption; Computer network; Product (mathematics); Internet of Things; Dot product; Distributed computing; Computer security; Artificial intelligence","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":[0.001968312,0.0005322128,0.001075771,0.0003976814,0.001099281,0.001382113,0.001254748,0.001027955,0.0006630496],"category_scores_gemma":[0.002551444,0.0002472963,0.0006692759,0.001086919,0.001256674,0.003958042,0.002447787,0.0009585466,0.0001385327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008396264,"about_ca_system_score_gemma":0.001190236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001915946,"about_ca_topic_score_gemma":0.001399545,"domain_scores_codex":[0.9977224,0.000516384,0.0001569401,0.0003372917,0.0008331159,0.0004336876],"domain_scores_gemma":[0.998427,0.0005327846,0.0001836798,0.000506927,0.0002700477,0.00007957261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002733637,0.0005861812,0.005572474,0.0006330057,0.0003647319,0.002712605,0.00189343,0.30644,0.1347797,0.2757499,0.01185692,0.2566775],"study_design_scores_gemma":[0.00009915895,0.0002985123,0.0007248666,0.0000180016,0.00006684152,0.0009600834,0.0002273473,0.9215621,0.02217152,0.04954911,0.004251811,0.00007060889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1127552,0.0009360051,0.881305,0.0004662809,0.00009331205,0.0002193625,0.0001404375,0.0004522165,0.003632196],"genre_scores_gemma":[0.9484168,0.0002689214,0.05013162,0.0002054488,0.00004304253,0.00005106165,0.00007490318,0.00001635987,0.0007918458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001968312,"threshold_uncertainty_score":0.01040953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0369021521216398,"score_gpt":0.303744507584078,"score_spread":0.2668423554624382,"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."}}