{"id":"W768808963","doi":"10.29012/jpc.v7i2.652","title":"Heterogeneous Differential Privacy","year":2017,"lang":"en","type":"preprint","venue":"Journal of Privacy and Confidentiality","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Differential privacy; Computer science; Cluster analysis; Personalization; Mechanism (biology); Task (project management); Personally identifiable information; Domain (mathematical analysis); Information sensitivity; Privacy software; Function (biology); Information privacy; Information retrieval; Data mining; Internet privacy; Computer security; World Wide Web; Artificial intelligence; Mathematics","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":[0.008204264,0.0008624496,0.001361476,0.001319253,0.001615446,0.004254977,0.002687122,0.001916662,0.003444084],"category_scores_gemma":[0.02763351,0.0004990149,0.00176179,0.002808939,0.003015276,0.007114015,0.005419348,0.002987838,0.0009165626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00238601,"about_ca_system_score_gemma":0.001424778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009024871,"about_ca_topic_score_gemma":0.0005971376,"domain_scores_codex":[0.9875383,0.004732689,0.0006869775,0.002710363,0.003462306,0.0008692786],"domain_scores_gemma":[0.9743382,0.01046938,0.001306999,0.01184976,0.001636529,0.000399031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000428472,0.0001909763,0.005163061,0.0003160636,0.0002355905,0.0005481288,0.0008282831,0.08866288,0.01089305,0.7275017,0.006544231,0.1586876],"study_design_scores_gemma":[0.00004451116,0.000137134,0.001756207,0.00004874172,0.00009622188,0.000910041,0.0002075649,0.33639,0.01179908,0.636695,0.01185913,0.00005640846],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02274808,0.0003283093,0.9683563,0.0009571054,0.00004911173,0.0001051394,0.000324339,0.000371887,0.006759686],"genre_scores_gemma":[0.8611587,0.0004915326,0.1310315,0.0006919963,0.0001727404,0.0002798084,0.0005739965,0.0001230911,0.005476481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008204264,"threshold_uncertainty_score":0.04338884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673858409058168,"score_gpt":0.3134868825341174,"score_spread":0.2667482984435358,"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."}}