{"id":"W2752484645","doi":"10.1109/access.2017.2748956","title":"PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential privacy; Computer science; Publication; Collusion; Cloud computing; Scheme (mathematics); Computer security; Privacy software; Context (archaeology); Information privacy; Data mining","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":[0.0007602384,0.0005270191,0.0006612951,0.0005180705,0.0008004963,0.00659777,0.1303232,0.0002807641,0.00003288833],"category_scores_gemma":[0.01936592,0.0004642599,0.0001162863,0.0006675278,0.0003816669,0.008516582,0.1459425,0.0009294372,0.00002118465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002033569,"about_ca_system_score_gemma":0.0002666385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183436,"about_ca_topic_score_gemma":0.0002976886,"domain_scores_codex":[0.99545,0.0001461687,0.00070898,0.001523266,0.0009446765,0.001226933],"domain_scores_gemma":[0.9759478,0.0003192656,0.0008318006,0.02240164,0.0002971969,0.0002023158],"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":[0.0003065472,0.001215372,0.8265104,0.0005672861,0.0002760312,0.0009307767,0.0005221845,0.000183267,0.01418204,0.003981645,0.09326462,0.05805982],"study_design_scores_gemma":[0.006051449,0.0001919985,0.2273144,0.001112997,0.00002299186,0.00003361262,0.00004966912,0.6853954,0.05322943,0.02380721,0.001226344,0.001564522],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6001295,0.00004799004,0.3785923,0.01824744,0.0009018623,0.000529278,0.00001314518,0.0009123966,0.0006260612],"genre_scores_gemma":[0.8519727,0.00000749996,0.1474356,0.0003077427,0.0001347569,0.00005395674,0.00001507222,0.00004566609,0.00002704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6852121,"threshold_uncertainty_score":0.9997809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1096448928351099,"score_gpt":0.3299019834175861,"score_spread":0.2202570905824763,"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."}}