{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00514885,0.0009741047,0.001839041,0.001116107,0.001654804,0.002420495,0.003161418,0.001514544,0.001158936],"category_scores_gemma":[0.01113946,0.0005302856,0.001278692,0.002574231,0.00146168,0.00783843,0.004741848,0.001678989,0.0003646539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00122772,"about_ca_system_score_gemma":0.00211784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002055638,"about_ca_topic_score_gemma":0.001713503,"domain_scores_codex":[0.9923763,0.003240101,0.0004306278,0.001210983,0.002168491,0.0005736491],"domain_scores_gemma":[0.9917754,0.002978593,0.001036725,0.003157243,0.0007112976,0.0003407844],"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":[0.0008374767,0.0007143079,0.005448412,0.00032619,0.0003252887,0.0006316415,0.001393231,0.5604863,0.01102286,0.0821401,0.005421777,0.3312525],"study_design_scores_gemma":[0.00003928919,0.00009390813,0.0005040324,0.00001486589,0.00004159148,0.0003291944,0.0001983865,0.9362173,0.004781479,0.05525617,0.002484189,0.00003958458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0619166,0.0005711683,0.9336143,0.0007844967,0.00004716909,0.000185577,0.0002468419,0.0004828738,0.002151029],"genre_scores_gemma":[0.8586258,0.00032981,0.1385888,0.0002506548,0.00006392653,0.000143605,0.0003133631,0.00006232597,0.001621706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00514885,"threshold_uncertainty_score":0.02723002,"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."}}