{"id":"W4308788998","doi":"10.1016/j.asoc.2022.109808","title":"Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Differential privacy; Granularity; Data publishing; Data mining; Information sensitivity; Cluster analysis; Information privacy; Representation (politics); Granular computing; Key (lock); Rough set; Computer security; Publishing; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01849709,0.0007839608,0.002269181,0.002362334,0.001392771,0.01062299,0.002768505,0.002790477,0.001889839],"category_scores_gemma":[0.07601289,0.0009966874,0.001282708,0.004806097,0.005082469,0.01593862,0.007800927,0.004333172,0.0003759958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002564156,"about_ca_system_score_gemma":0.001991818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006811227,"about_ca_topic_score_gemma":0.0006232145,"domain_scores_codex":[0.9760819,0.01010045,0.001769993,0.002319329,0.008009658,0.001718631],"domain_scores_gemma":[0.9067557,0.05265645,0.005166178,0.03057276,0.003193131,0.001655729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001622838,0.0003033146,0.005193959,0.0005523167,0.0002874875,0.0004436348,0.0009930808,0.1465788,0.01104301,0.6893608,0.002652276,0.1409686],"study_design_scores_gemma":[0.000102705,0.0002448798,0.001540322,0.0001468038,0.0001600282,0.0005800114,0.0005093323,0.3035329,0.008533267,0.6808942,0.003696973,0.00005845212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1245043,0.001552259,0.8597531,0.005638917,0.0001381212,0.0001711896,0.0003470145,0.0003545357,0.007540493],"genre_scores_gemma":[0.9145391,0.0005611577,0.08275733,0.0003470508,0.0001822869,0.00009666847,0.0001607072,0.00007814964,0.001277486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01849709,"threshold_uncertainty_score":0.09782308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040926566821935,"score_gpt":0.3064421215527895,"score_spread":0.202349464870596,"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."}}