{"id":"W2964208174","doi":"10.1109/tsipn.2018.2866342","title":"Privacy-Preserving Average Consensus: Privacy Analysis and Algorithm Design","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Information privacy; Node (physics); Variance (accounting); Differential privacy; Noise (video); Privacy protection; Algorithm; Data mining; Artificial intelligence; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005505085,0.001029308,0.001550762,0.001234637,0.001171879,0.002609933,0.002640171,0.002187013,0.001619041],"category_scores_gemma":[0.01911201,0.0006790371,0.001294706,0.002161083,0.002269688,0.004365453,0.003047774,0.002756597,0.0004745466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002399151,"about_ca_system_score_gemma":0.002843805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472345,"about_ca_topic_score_gemma":0.0007330924,"domain_scores_codex":[0.9941862,0.002257922,0.0003125323,0.001399544,0.001441813,0.000401926],"domain_scores_gemma":[0.9916414,0.004635348,0.0008320769,0.001301602,0.001359908,0.0002295801],"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":[0.000170682,0.0000813542,0.001096264,0.0002136642,0.0001165894,0.0001790835,0.0002920133,0.6372231,0.005205425,0.2723401,0.002690494,0.08039121],"study_design_scores_gemma":[0.00001112875,0.00004932423,0.00007800793,0.00001288839,0.00001206964,0.00007321936,0.00002453131,0.9248508,0.00158485,0.07230879,0.0009790818,0.00001532933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002191632,0.0001661704,0.9964316,0.0002397255,0.00001786403,0.00003514863,0.00002638755,0.00006977208,0.0008218047],"genre_scores_gemma":[0.661424,0.001666381,0.3314654,0.0003914655,0.0002722131,0.0006013681,0.0002673854,0.000120404,0.003791382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005505085,"threshold_uncertainty_score":0.02911401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01856112215833846,"score_gpt":0.2494712361959399,"score_spread":0.2309101140376014,"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."}}