{"id":"W4229683366","doi":"10.1109/jiot.2017.2766701","title":"Efficient and Privacy-Preserving Proximity Detection Schemes for Social Applications","year":2017,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Ciphertext; Range query (database); Server; Encryption; Cryptography; Location-based service; Computer security; Information sensitivity; Popularity; Web search query; Web query classification; Information retrieval; Computer network; Search engine","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":[0.002137368,0.000693017,0.001118051,0.001284707,0.00167595,0.001567663,0.002181229,0.001273479,0.001471672],"category_scores_gemma":[0.008460059,0.0004227525,0.0009433469,0.002289108,0.001147032,0.005055427,0.005031092,0.00154931,0.0007526604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147616,"about_ca_system_score_gemma":0.001264733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389109,"about_ca_topic_score_gemma":0.001000392,"domain_scores_codex":[0.9945725,0.001571181,0.000388854,0.00083124,0.002171202,0.0004650528],"domain_scores_gemma":[0.9936645,0.001514652,0.0007483306,0.002966009,0.0008900378,0.0002165648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0017319,0.0004979447,0.005955736,0.0006078426,0.0002425632,0.0009836562,0.002042235,0.1173973,0.07179721,0.1676309,0.01419788,0.6169149],"study_design_scores_gemma":[0.0001682113,0.0005594999,0.002082515,0.00005016082,0.0001170937,0.001498732,0.000653644,0.8528203,0.03613019,0.07661856,0.02916878,0.0001321814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05064754,0.001668747,0.9413936,0.0006117424,0.0001086304,0.0002988688,0.0002574885,0.001388662,0.00362476],"genre_scores_gemma":[0.8500008,0.0007537288,0.1451707,0.0002472012,0.000111995,0.0002525041,0.0003885397,0.00005005911,0.003024485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002181229,"threshold_uncertainty_score":0.0113036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04066354850310649,"score_gpt":0.3107029736514063,"score_spread":0.2700394251482998,"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."}}