{"id":"W1995571756","doi":"10.1002/cpe.3484","title":"SDIVIP<sup>2</sup>: shared data integrity verification with identity privacy preserving in mobile clouds","year":2015,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Correctness; Cloud computing; Data integrity; Mobile device; Mobile computing; Computer security; Mobile cloud computing; Cloud storage; Computer network; Operating system","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":[0.000830299,0.0001549268,0.0001667757,0.00008643862,0.0002058188,0.0006479821,0.001036945,0.00006416074,0.000005477874],"category_scores_gemma":[0.001218247,0.0001514834,0.000009526459,0.0006263076,0.0001539828,0.01189946,0.001201974,0.0003101441,0.00001050834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004413573,"about_ca_system_score_gemma":0.0002098322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004105486,"about_ca_topic_score_gemma":0.00004521536,"domain_scores_codex":[0.998118,0.0002375852,0.0003199066,0.0007273057,0.0003707818,0.0002264039],"domain_scores_gemma":[0.998213,0.0003626578,0.0001902168,0.0007936548,0.0002676036,0.0001729017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003410134,0.001791363,0.03544737,0.000294689,0.00008671313,0.000131268,0.6170591,0.004464228,0.0001901304,0.1173784,0.00694475,0.215871],"study_design_scores_gemma":[0.001149101,0.0002461498,0.005725533,0.0001121487,0.00002094572,0.0001481175,0.01578441,0.9465525,0.00003375084,0.003578321,0.02624861,0.000400452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4741385,0.002002098,0.5218867,0.001006354,0.0001536504,0.00037973,0.00003605696,0.000098741,0.0002982258],"genre_scores_gemma":[0.9741068,0.0002335412,0.02524526,0.000164266,0.00003468322,0.00007090627,0.0001325713,0.000005398115,0.000006605813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9420882,"threshold_uncertainty_score":0.8626819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029223052533108,"score_gpt":0.3762520229677956,"score_spread":0.2733297177144848,"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."}}