{"id":"W2133412796","doi":"10.1002/wcm.796","title":"MDPA: multidimensional privacy‐preserving aggregation scheme for wireless sensor networks","year":2009,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"","keywords":"Data aggregator; Computer science; Wireless sensor network; Energy consumption; Efficient energy use; Scheme (mathematics); Computer network; Wireless; Information privacy; Compressed sensing; Data mining; Distributed computing; Algorithm; Computer security; Telecommunications; Mathematics; Engineering","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":[0.001414819,0.0003886311,0.0007392784,0.0006767139,0.0006417362,0.0007700084,0.001052347,0.0006645581,0.0007631868],"category_scores_gemma":[0.002866029,0.0001768392,0.0004256589,0.001165392,0.0005339765,0.00197302,0.001897811,0.0009402099,0.0002724373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004664393,"about_ca_system_score_gemma":0.0006132688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002936728,"about_ca_topic_score_gemma":0.0002514458,"domain_scores_codex":[0.998406,0.0005529647,0.0001284666,0.0002180528,0.0005969488,0.0000975783],"domain_scores_gemma":[0.9983637,0.0003557265,0.0002556402,0.000688704,0.0002650148,0.00007124062],"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":[0.001096388,0.0002377074,0.002581598,0.0004590486,0.0002421717,0.0006420035,0.0006946097,0.1951132,0.1036674,0.1064424,0.0116789,0.5771446],"study_design_scores_gemma":[0.00006914993,0.0003097495,0.0006907845,0.00002653842,0.00004281056,0.0006074201,0.00007267731,0.9134254,0.0338771,0.03691998,0.01390741,0.00005094091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02363805,0.0007438219,0.9732538,0.00047662,0.0001007996,0.00008334148,0.0001398272,0.0005418478,0.001021914],"genre_scores_gemma":[0.6829917,0.0005602648,0.313697,0.0002983953,0.0001453236,0.0001828219,0.000308289,0.00002808016,0.001788204],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001414819,"threshold_uncertainty_score":0.00748235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996527085973741,"score_gpt":0.2735287089386718,"score_spread":0.2535634380789343,"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."}}