{"id":"W4383503594","doi":"10.1109/tifs.2023.3293416","title":"Efficient and Privacy-Preserving Aggregated Reverse kNN Query Over Crowd-Sensed Data","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Science Basic Research Program of Shaanxi Province; Higher Education Discipline Innovation Project; Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Scheme (mathematics); Information privacy; Random oracle; Oracle; Private information retrieval; Query optimization; Web query classification; Privacy software; Data mining; Web search query; Information retrieval; Encryption; Computer security; Public-key cryptography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006027702,0.0002063473,0.0001943838,0.0003890959,0.0004271568,0.0003845676,0.004767394,0.0001722353,0.00001175792],"category_scores_gemma":[0.0008792644,0.00020194,0.00003759561,0.0009055786,0.0001626737,0.002100096,0.002580123,0.0003941261,0.00004385811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004589662,"about_ca_system_score_gemma":0.00005362849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363524,"about_ca_topic_score_gemma":0.00003295268,"domain_scores_codex":[0.9984093,0.00004709999,0.0004110047,0.0003948635,0.0003979911,0.0003397062],"domain_scores_gemma":[0.9944687,0.0001970245,0.0001597061,0.00495225,0.0001095398,0.0001127818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002120972,0.000272422,0.0002528724,0.0008850025,0.0003518295,0.00006857628,0.01040029,0.00895867,0.0005828161,0.01731486,0.5911807,0.3695198],"study_design_scores_gemma":[0.0005373366,0.0000420472,0.0003642833,0.00006584433,0.00001351876,0.00002072219,0.0001319523,0.9698958,0.001063655,0.02132839,0.006294836,0.0002415637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2170667,0.00003157557,0.7724181,0.007292008,0.0006816534,0.0004013155,0.0005390786,0.001318716,0.0002509286],"genre_scores_gemma":[0.975235,0.0003916072,0.02373996,0.0004259197,0.0000161506,0.00001794113,0.0001413271,0.00001250633,0.00001962126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9609372,"threshold_uncertainty_score":0.8859082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02823299154389445,"score_gpt":0.2634371240388225,"score_spread":0.235204132494928,"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."}}