{"id":"W2970585478","doi":"10.1109/bigdatasecurity-hpsc-ids.2019.00021","title":"An Analysis of Differential Privacy Research in Location Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Differential privacy; Computer science; Field (mathematics); Noise (video); Privacy software; Data collection; Information privacy; Data sharing; Aggregate (composite); Data mining; Data science; Differential (mechanical device); Adversary; Computer security; Artificial intelligence; Engineering","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0187625,0.001006572,0.001501234,0.006163975,0.002561769,0.008331794,0.002889694,0.003355226,0.003874497],"category_scores_gemma":[0.06929119,0.000868462,0.002598017,0.01320065,0.007070313,0.01858572,0.005617197,0.005968377,0.0009520874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007992364,"about_ca_system_score_gemma":0.00259629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002212391,"about_ca_topic_score_gemma":0.001142379,"domain_scores_codex":[0.9721454,0.01247129,0.001278673,0.003630255,0.009048692,0.00142562],"domain_scores_gemma":[0.9116128,0.0668505,0.004385347,0.008233504,0.008018902,0.0008989618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000556521,0.00004322318,0.002484218,0.0002729084,0.00006735574,0.0002488167,0.0004505323,0.01455783,0.0003704753,0.9367237,0.003474964,0.0412504],"study_design_scores_gemma":[0.00001920383,0.00009797722,0.001703934,0.0003600452,0.00006887691,0.0009309159,0.000407762,0.1193684,0.00155911,0.8364373,0.03898755,0.00005896891],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02399296,0.03307995,0.8626009,0.03033616,0.000652568,0.0001911026,0.0005676534,0.0001861883,0.04839255],"genre_scores_gemma":[0.7990848,0.04662854,0.1324478,0.004865083,0.003937402,0.0005295863,0.0007694502,0.0001992473,0.01153808],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.993836,"threshold_uncertainty_score":0.09922677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.143277881991835,"score_gpt":0.4046114064386273,"score_spread":0.2613335244467923,"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."}}