{"id":"W3135807060","doi":"10.1109/tmc.2021.3062775","title":"Privacy-Preserving Streaming Truth Discovery in Crowdsourcing With Differential Privacy","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Differential privacy; Crowdsourcing; Computer science; Internet privacy; Privacy protection; Information privacy; Privacy software; Computer security; World Wide Web; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003209683,0.0004454458,0.0005060842,0.0003646101,0.0006464865,0.0009848473,0.0009571517,0.0001367195,0.00002487075],"category_scores_gemma":[0.0000382592,0.0004344387,0.000198854,0.001219713,0.00007321304,0.0009023619,0.00007145839,0.0008176031,0.00001064399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002180486,"about_ca_system_score_gemma":0.0002214187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228932,"about_ca_topic_score_gemma":0.00008292212,"domain_scores_codex":[0.9965435,0.0002728548,0.0006262697,0.001166775,0.0005514035,0.0008392418],"domain_scores_gemma":[0.9973765,0.0007177087,0.0001940125,0.001405805,0.0001317404,0.0001742242],"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":[0.00005302757,0.0006821256,0.0009551545,0.0001483638,0.00009835717,0.0004553277,0.005302641,0.8310135,0.01715065,0.0002648424,0.00001812026,0.1438579],"study_design_scores_gemma":[0.002436145,0.0003183417,0.003350203,0.001678905,0.00005598499,0.0003615376,0.001322438,0.8211768,0.1677723,0.0001659355,0.0002087439,0.001152716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4813098,0.00004236402,0.5175817,0.00005509484,0.0004906218,0.0001702643,0.000003000761,0.0002131252,0.0001340514],"genre_scores_gemma":[0.9779572,0.00001109,0.02156784,0.00007548543,0.0001287531,0.00003194127,0.000004544856,0.00005680313,0.0001663056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4966474,"threshold_uncertainty_score":0.9998108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116516055892134,"score_gpt":0.2347180059483407,"score_spread":0.2230664003591273,"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."}}