{"id":"W2591915039","doi":"10.1109/access.2017.2677520","title":"A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":477,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Data aggregator; Paillier cryptosystem; Homomorphic encryption; Edge computing; Differential privacy; Computer network; Edge device; Location awareness; Internet of Things; Cloud computing; Information privacy; Encryption; Security analysis; Enhanced Data Rates for GSM Evolution; Aggregate (composite); Computer security; Wireless sensor network; Public-key cryptography; Data mining; Telecommunications","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":["metaresearch","metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001000437,0.0003307403,0.0003848137,0.0001950385,0.001273447,0.003280078,0.2200048,0.0002371002,0.0000120762],"category_scores_gemma":[0.03495834,0.0003208841,0.00007838873,0.0003146667,0.0001836846,0.006269048,0.2921411,0.0003615023,0.00004088478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008947832,"about_ca_system_score_gemma":0.0001427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001292692,"about_ca_topic_score_gemma":0.00006119527,"domain_scores_codex":[0.9966437,0.00005671547,0.0005031595,0.001526963,0.0005257108,0.0007438038],"domain_scores_gemma":[0.9544311,0.000400759,0.0007717364,0.04401566,0.0002652538,0.0001155296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004274477,0.0001881999,0.003843365,0.0002867277,0.0001476301,0.00002817519,0.0001608748,0.00003599937,0.01156949,0.002797201,0.7886531,0.1922465],"study_design_scores_gemma":[0.0008626276,0.00004778554,0.002452215,0.0002549933,0.00001553823,0.000007154975,0.000004410176,0.7117869,0.1318473,0.1414221,0.0107744,0.0005245742],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06781898,0.0001494553,0.9049313,0.02193194,0.002177108,0.0007512291,0.00008729032,0.001184626,0.0009680622],"genre_scores_gemma":[0.5385146,0.00003391474,0.4607018,0.0002021478,0.0003484692,0.00004380762,0.0000564854,0.00003095495,0.00006778619],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7778786,"threshold_uncertainty_score":0.9999243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1231242905876708,"score_gpt":0.3827469961179321,"score_spread":0.2596227055302613,"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."}}