{"id":"W2015569667","doi":"10.1145/2339530.2339564","title":"Differentially private transit data publication","year":2012,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":250,"is_retracted":false,"has_abstract":true,"ca_institutions":"Société de Transport de Montréal; Concordia University","funders":"","keywords":"Computer science; Data publishing; Trie; Differential privacy; Scalability; Data mining; Volume (thermodynamics); Consistency (knowledge bases); Data structure; Granularity; Tree (set theory); Data consistency; Smart card; Trajectory; Big data; Publishing; Database; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004578275,0.0001042331,0.0000948376,0.00009368238,0.00007091779,0.000230621,0.06362817,0.00007856834,0.0001083267],"category_scores_gemma":[0.00523663,0.00008722935,0.00001731362,0.0003797135,0.00004213739,0.004193987,0.1201102,0.000132919,0.0002427673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002255148,"about_ca_system_score_gemma":0.00001986619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000159899,"about_ca_topic_score_gemma":0.000005715682,"domain_scores_codex":[0.998741,0.00003565803,0.0001810722,0.0004091348,0.000253641,0.0003795373],"domain_scores_gemma":[0.9784911,0.00005245617,0.00005819673,0.0212761,0.00004000181,0.00008216809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001544021,0.0001476262,0.003427657,0.00001142699,0.00002410578,6.409234e-7,0.00004590569,1.212144e-7,0.002251459,0.1337474,0.6836881,0.176654],"study_design_scores_gemma":[0.0006878305,0.00005253133,0.07762037,0.00002324605,0.00002667734,0.00003899067,0.00001284884,0.2989788,0.02720837,0.2248147,0.3696173,0.0009183339],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0035028,0.00006569854,0.9395172,0.05100464,0.000322728,0.0001150735,0.00001864898,0.001504282,0.00394893],"genre_scores_gemma":[0.4105255,0.00002222973,0.5887533,0.0003533151,0.00005512316,0.00000804124,0.0001421413,0.000007076759,0.0001332862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4070227,"threshold_uncertainty_score":0.941438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07720000497326897,"score_gpt":0.2951806278908875,"score_spread":0.2179806229176185,"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."}}