{"id":"W2791539234","doi":"10.1515/popets-2018-0010","title":"Privacy-preserving Wi-Fi Analytics","year":2018,"lang":"en","type":"article","venue":"Proceedings on Privacy Enhancing Technologies","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Differential privacy; Computer science; Leverage (statistics); Analytics; Intersection (aeronautics); Bloom filter; Computer security; Data science; Data mining; Computer network; Machine learning","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":[0.006371342,0.001209566,0.001408225,0.001888194,0.001510585,0.005088815,0.003190385,0.00152577,0.001501484],"category_scores_gemma":[0.02787518,0.0006741372,0.001066969,0.003918502,0.00194666,0.01025699,0.005232953,0.003241754,0.001048437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679353,"about_ca_system_score_gemma":0.002696171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001401464,"about_ca_topic_score_gemma":0.001326492,"domain_scores_codex":[0.9899318,0.00287602,0.0009007372,0.002069535,0.003512293,0.0007095709],"domain_scores_gemma":[0.9703561,0.007909534,0.002760846,0.01603308,0.002498117,0.0004423527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001922632,0.0005898043,0.03191249,0.0005135712,0.0003100856,0.0006250525,0.001304616,0.4361733,0.03616806,0.1815664,0.0116124,0.2973016],"study_design_scores_gemma":[0.00002868356,0.00007312514,0.00112897,0.00003271544,0.00002553941,0.0002475522,0.0002227741,0.8559207,0.01967812,0.1166458,0.00596131,0.00003471952],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04581791,0.0002226753,0.9478869,0.0008257651,0.00004478615,0.0001271123,0.0011276,0.001703628,0.002243588],"genre_scores_gemma":[0.7849953,0.0002784669,0.2098242,0.0003152201,0.0001105547,0.0001972966,0.002218675,0.0001220385,0.0019383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006371342,"threshold_uncertainty_score":0.03369534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072250435332384,"score_gpt":0.2800936819721157,"score_spread":0.2493711776187919,"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."}}