{"id":"W4214771680","doi":"10.1109/tdsc.2022.3153759","title":"Towards Efficient and Privacy-Preserving Interval Skyline Queries Over Time Series Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Skyline; Encryption; Homomorphic encryption; Security analysis; Information privacy; Data mining; Database; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005393469,0.0001920982,0.0002107443,0.0001559579,0.0009553999,0.000458077,0.00132822,0.00002910785,0.0001193052],"category_scores_gemma":[0.00000826039,0.0001914039,0.00003548212,0.000341171,0.0000513041,0.0008955138,0.0006354056,0.0003237907,0.000005680408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002908015,"about_ca_system_score_gemma":0.00003550885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009750657,"about_ca_topic_score_gemma":0.00001841461,"domain_scores_codex":[0.9983483,0.0001004692,0.0002468947,0.000658982,0.0003397683,0.0003055609],"domain_scores_gemma":[0.9988832,0.00007900639,0.00007761282,0.0008495844,0.00002594335,0.00008466812],"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":[0.0001998524,0.0009033781,0.000156549,0.0004038291,0.0004796387,0.000351572,0.009704503,0.1296194,0.001101665,0.006064841,0.008151985,0.8428628],"study_design_scores_gemma":[0.000398733,0.0001594569,0.0001077386,0.0000324855,0.00002399492,0.00007548649,0.0002028112,0.9844869,0.0005021432,0.0001960816,0.01356257,0.0002516245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06092755,0.0002293583,0.9364806,0.0009482461,0.0005969253,0.0001877461,0.0001379317,0.0002100183,0.0002815571],"genre_scores_gemma":[0.9712871,0.00004451233,0.02762222,0.0002578578,0.00006750863,0.000008147986,0.00004443771,0.0000198377,0.0006483819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9103596,"threshold_uncertainty_score":0.7805223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967383066052402,"score_gpt":0.2501485958531611,"score_spread":0.230474765192637,"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."}}