{"id":"W2997423112","doi":"10.1109/access.2019.2961270","title":"Achieving Privacy-Preserving Subset Aggregation in Fog-Enhanced IoT","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; New Brunswick Innovation Foundation","keywords":"Computer science; Internet of Things; Information privacy; Computer security; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006901649,0.0002561613,0.0003162973,0.0004294185,0.00009709302,0.0006612394,0.05928372,0.0002043374,0.00005746512],"category_scores_gemma":[0.007382597,0.0002613809,0.00006198891,0.001505113,0.00004991307,0.003475256,0.07758939,0.0004996773,0.0002002235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817996,"about_ca_system_score_gemma":0.00008264247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002028312,"about_ca_topic_score_gemma":0.0000997609,"domain_scores_codex":[0.9973399,0.0001221683,0.0004591922,0.000929835,0.0005107059,0.0006381802],"domain_scores_gemma":[0.9886551,0.0003150242,0.0002554659,0.0106164,0.00008948865,0.00006847118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009376508,0.0004776875,0.3858658,0.000668394,0.0001115213,0.000148545,0.001320582,0.002633786,0.1358495,0.003717281,0.1308228,0.3382903],"study_design_scores_gemma":[0.001982484,0.0001363834,0.1204488,0.000928737,0.00001062615,0.00002048666,0.00005712455,0.3628669,0.3050349,0.2044042,0.002705839,0.001403514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8500012,0.0001518446,0.1394131,0.006250229,0.001145723,0.0004643895,0.000005690502,0.0008162337,0.001751666],"genre_scores_gemma":[0.9473773,0.00005935255,0.05209761,0.0002594421,0.00006156088,0.0000459173,0.000008410819,0.00002420328,0.00006621124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3602331,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03459198063690118,"score_gpt":0.3077357538314907,"score_spread":0.2731437731945895,"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."}}