{"id":"W4390421908","doi":"10.1109/tmc.2023.3348136","title":"Privacy-Preserving Location-Based Advertising via Longitudinal Geo-Indistinguishability","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Obfuscation; Computer science; Inference; Differential privacy; Computer security; Data mining; 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":[0.003448782,0.0006851569,0.001166795,0.0006392271,0.001223163,0.002224282,0.001526503,0.001404919,0.001488412],"category_scores_gemma":[0.01232661,0.0005227806,0.0009030238,0.001134883,0.002008749,0.005794478,0.00551429,0.002411366,0.0007004894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000945292,"about_ca_system_score_gemma":0.001177032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000828578,"about_ca_topic_score_gemma":0.000592204,"domain_scores_codex":[0.9957085,0.001486952,0.0002587922,0.0006738272,0.001269771,0.0006021798],"domain_scores_gemma":[0.9872469,0.003863232,0.001437683,0.006408426,0.0007018144,0.0003420445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003450202,0.0004905036,0.01131877,0.0003075168,0.0001853854,0.001406475,0.001532568,0.2937074,0.05329588,0.3248648,0.008065362,0.3013752],"study_design_scores_gemma":[0.0000949446,0.0002405701,0.001002613,0.00002683937,0.00005542592,0.000757541,0.0001212074,0.8542674,0.03278365,0.103868,0.006706605,0.00007526411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052807,0.0004537663,0.8858752,0.0009151108,0.00006189325,0.00009915938,0.0002454668,0.002373273,0.004695559],"genre_scores_gemma":[0.9605951,0.0001527386,0.03704716,0.0001841166,0.00003986683,0.00006926049,0.00013877,0.00005105541,0.001722001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003448782,"threshold_uncertainty_score":0.01823914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0295127119607585,"score_gpt":0.2908800245725671,"score_spread":0.2613673126118086,"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."}}