{"id":"W4323343874","doi":"10.14778/3579075.3579086","title":"On the Risks of Collecting Multidimensional Data Under Local Differential Privacy","year":2023,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Differential privacy; Computer science; Robustness (evolution); Inference; Data mining; Population; Identification (biology); Hash function; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0008335958,0.0001766807,0.0002132426,0.0001358631,0.0002515823,0.0000604339,0.03049794,0.00007414406,0.00001476966],"category_scores_gemma":[0.009758117,0.0001003618,0.00007434512,0.001057106,0.0002629001,0.0002890536,0.1357796,0.0003201308,0.00001408382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000805585,"about_ca_system_score_gemma":0.00005597041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008053943,"about_ca_topic_score_gemma":0.000002020102,"domain_scores_codex":[0.997899,0.00002175037,0.0003687756,0.000532022,0.0008329331,0.0003455141],"domain_scores_gemma":[0.99495,0.0006005464,0.0003721316,0.003904855,0.0001373243,0.00003512713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008735013,0.0004855727,0.002172773,0.0001780759,0.0003221131,0.00000200688,0.0005991977,0.0002819849,0.09472903,0.2310154,0.6575527,0.01257387],"study_design_scores_gemma":[0.0007391172,0.0001383324,0.006415383,0.0002991284,0.00003473987,0.000008855093,0.0004170028,0.3499951,0.3440934,0.2970413,0.0005736736,0.0002438945],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9251988,0.00004524593,0.01933717,0.05223943,0.0007594444,0.0009293079,0.00008596171,0.0005690203,0.000835656],"genre_scores_gemma":[0.9860856,0.00002750997,0.01365298,0.0001088757,0.00002484568,0.0000282145,0.000004969456,0.00001322964,0.0000537662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.656979,"threshold_uncertainty_score":0.9985831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1558333964085812,"score_gpt":0.3367118207503328,"score_spread":0.1808784243417517,"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."}}