{"id":"W2594248285","doi":"10.3390/s17030500","title":"PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Scalability; Computer science; Data aggregator; Scheme (mathematics); Computer security; Information privacy; Variance (accounting); Risk analysis (engineering); Business; Wireless sensor network; Computer network; Database","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":[0.00262504,0.0007601083,0.001352993,0.0009388323,0.0009625491,0.001460643,0.002374348,0.00106175,0.00135497],"category_scores_gemma":[0.005389772,0.0004252402,0.00106767,0.001964825,0.0009310109,0.003482481,0.003960575,0.001513625,0.0006093116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008509435,"about_ca_system_score_gemma":0.001527278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001448395,"about_ca_topic_score_gemma":0.0009715817,"domain_scores_codex":[0.9967977,0.0009533772,0.0002951341,0.0006268365,0.0009670497,0.0003599125],"domain_scores_gemma":[0.9976369,0.000629859,0.0002965491,0.0009146639,0.0004055864,0.0001164784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001611491,0.0002879298,0.00435002,0.0005650633,0.0003598632,0.0006815632,0.000644577,0.3588451,0.0397319,0.09209133,0.02283072,0.4780003],"study_design_scores_gemma":[0.00005712932,0.0002914185,0.0007685401,0.00001676931,0.00004894887,0.0004151706,0.00008521332,0.9508401,0.007636306,0.02713722,0.01265572,0.00004740086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01668507,0.0005825431,0.9789507,0.0003741139,0.0001365546,0.0001435358,0.000363346,0.001412704,0.001351346],"genre_scores_gemma":[0.7827426,0.0006488658,0.2113187,0.0004070348,0.0002351234,0.0002684152,0.0009724451,0.00007653615,0.003330368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00262504,"threshold_uncertainty_score":0.0138827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339217121635684,"score_gpt":0.3337027196684155,"score_spread":0.199781007504847,"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."}}