{"id":"W4387475765","doi":"10.1109/jiot.2023.3323436","title":"Efficient Privacy-Preserving Task Allocation With Secret Sharing for Vehicular Crowdsensing","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Crowdsensing; Computer science; Task (project management); Computer security; Secret sharing; Computer network; Privacy protection; Cryptography","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":[],"consensus_categories":[],"category_scores_codex":[0.001682932,0.000211264,0.0002925855,0.000352486,0.000244638,0.000636822,0.001349572,0.00008600044,0.000003807414],"category_scores_gemma":[0.000253928,0.0001797571,0.000166762,0.0004631797,0.00005849614,0.0004193549,0.0003773025,0.0003874932,0.00001010464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038444,"about_ca_system_score_gemma":0.00008613186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005937785,"about_ca_topic_score_gemma":0.000002569111,"domain_scores_codex":[0.9979204,0.00005690882,0.0005348197,0.000439503,0.0005542653,0.0004940848],"domain_scores_gemma":[0.9981701,0.0001858794,0.0004908423,0.0005807451,0.0004224127,0.0001499675],"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":[0.0002231722,0.000156661,0.001757526,0.0004840576,0.0004539488,0.000368872,0.04063603,0.5543879,0.3639014,0.002858288,0.00860894,0.02616322],"study_design_scores_gemma":[0.0005234377,0.0001616516,0.0002824894,0.0009043926,0.00002470972,0.0005179601,0.0001818737,0.9417452,0.05398883,0.0009820681,0.0004664843,0.0002209254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5345103,0.00003786571,0.4641346,0.0004506262,0.0005212481,0.0001132017,3.654517e-7,0.0001238046,0.0001080797],"genre_scores_gemma":[0.9630699,0.000004148388,0.03623118,0.0001518177,0.000187363,0.00000479644,0.000001882835,0.00003325073,0.0003156757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4285597,"threshold_uncertainty_score":0.733028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876827518296159,"score_gpt":0.2506260669904957,"score_spread":0.2318577918075341,"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."}}