{"id":"W2938767118","doi":"10.1109/tdsc.2018.2861403","title":"Disclose More and Risk Less: Privacy Preserving Online Social Network Data Sharing","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Institute of Chemistry, Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Social network (sociolinguistics); Heuristics; Private information retrieval; Permission; Information privacy; Knapsack problem; Inference; Computer security; Social media; Internet privacy; World Wide Web; Artificial intelligence; Algorithm","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":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004705175,0.0003031124,0.0003506551,0.00008591064,0.001121193,0.0005145984,0.01370688,0.000175036,0.000006119581],"category_scores_gemma":[0.0005585772,0.0003018844,0.00004939741,0.0006107757,0.0001070019,0.001187095,0.008815304,0.001010385,0.000003010957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002869298,"about_ca_system_score_gemma":0.00004286671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001551463,"about_ca_topic_score_gemma":0.000103709,"domain_scores_codex":[0.9972879,0.00009527018,0.0003937594,0.001345557,0.0003166363,0.0005609059],"domain_scores_gemma":[0.9957098,0.000286265,0.0001852989,0.003604848,0.00004595613,0.0001678145],"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":[0.0001239032,0.0004499652,0.006722832,0.0006026624,0.0006476645,0.0002232158,0.007394413,0.03417304,0.0004144444,0.001056413,0.03627939,0.9119121],"study_design_scores_gemma":[0.0004676131,0.00006172733,0.0006048307,0.0001043948,0.00005076458,0.00002661746,0.0002664385,0.9912636,0.0002366063,0.005682689,0.000896097,0.0003385906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1596867,0.0004625238,0.826596,0.01186953,0.0002308715,0.0002059909,0.0001785017,0.0007388816,0.00003106235],"genre_scores_gemma":[0.8280205,0.0003887266,0.1709587,0.0003228888,0.0002411376,0.000003254697,0.0000298358,0.00002916514,0.000005723054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9570906,"threshold_uncertainty_score":0.9999433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07697807668855826,"score_gpt":0.3012775501062641,"score_spread":0.2242994734177058,"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."}}