{"id":"W3197627620","doi":"10.2196/26914","title":"Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Differential privacy; Categorical variable; Computer science; Algorithm; Data mining; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01533887,0.0009236443,0.001096017,0.001413657,0.0005904788,0.001726475,0.00184412,0.002051481,0.001115184],"category_scores_gemma":[0.04447869,0.0003171391,0.001067568,0.00127054,0.001915287,0.001996316,0.003261014,0.002372005,0.0002469518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00180153,"about_ca_system_score_gemma":0.002281651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003361752,"about_ca_topic_score_gemma":0.00193848,"domain_scores_codex":[0.9955538,0.002529391,0.0002866403,0.0005323819,0.0008355223,0.0002623064],"domain_scores_gemma":[0.9677701,0.02408139,0.001231474,0.00347329,0.00303061,0.0004132662],"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":[0.0002771776,0.0002416683,0.006445511,0.0001149277,0.00009495037,0.0001995061,0.0001861831,0.8836619,0.001991548,0.01214831,0.001503046,0.09313523],"study_design_scores_gemma":[0.00001432607,0.00003343463,0.000278459,0.00001056703,0.000004858005,0.00004751213,0.00002511716,0.9946942,0.0008422386,0.003864006,0.0001806832,0.000004605594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08451927,0.000663113,0.9117636,0.0009472312,0.00004136046,0.0002067118,0.00009718879,0.0006140664,0.00114751],"genre_scores_gemma":[0.6542884,0.0003601153,0.34334,0.0003204197,0.00004021638,0.0003125494,0.0003759354,0.00007494487,0.0008874764],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01533887,"threshold_uncertainty_score":0.08112067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922403675018285,"score_gpt":0.2908751744290082,"score_spread":0.2716511376788254,"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."}}