{"id":"W4327599133","doi":"10.3390/app13063770","title":"FSopt_k: Finding the Optimal Anonymization Level for a Social Network Graph","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph; k-anonymity; Privacy protection; Theoretical computer science; Social graph; Anonymity; Information loss; Computer security; Social media; World Wide Web; 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":[0.001382734,0.001496656,0.001104119,0.001906802,0.001671825,0.001586389,0.001266116,0.001487677,0.003016117],"category_scores_gemma":[0.004909336,0.0004359687,0.001214193,0.001441004,0.001188457,0.003230477,0.00176392,0.001031709,0.0008795405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001921682,"about_ca_system_score_gemma":0.002766741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00321526,"about_ca_topic_score_gemma":0.004252214,"domain_scores_codex":[0.9983538,0.0004212231,0.00009167788,0.0005553822,0.000336592,0.0002413369],"domain_scores_gemma":[0.9982146,0.0006565006,0.0002691337,0.0004876626,0.0002658313,0.0001062513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007861747,0.0004069481,0.007329429,0.0005197926,0.0002734922,0.0003585258,0.0008641727,0.4155013,0.02882676,0.05030862,0.02721599,0.4676087],"study_design_scores_gemma":[0.00008580378,0.0002052899,0.001262023,0.00006045635,0.00006447548,0.0005417251,0.0004818396,0.9209338,0.01553624,0.05217532,0.008598745,0.0000544289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07363261,0.0004960824,0.9139556,0.001024148,0.0001133397,0.0005288307,0.0008835449,0.002480351,0.006885538],"genre_scores_gemma":[0.4204044,0.000325487,0.5729348,0.0002965558,0.00005227701,0.0003496353,0.001512127,0.0003164162,0.003808344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00321526,"threshold_uncertainty_score":0.01394284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1239265401694021,"score_gpt":0.3244650942543809,"score_spread":0.2005385540849788,"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."}}