{"id":"W2948425938","doi":"10.1109/tii.2019.2920402","title":"Game Theoretical Analysis on Encrypted Cloud Data Deduplication","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; National Postdoctoral Program for Innovative Talents; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; Academy of Finland; National Natural Science Foundation of China","keywords":"Data deduplication; Cloud computing; Computer science; Incentive; Service provider; Robustness (evolution); Encryption; Incentive compatibility; Profitability index; Software deployment; Game theory; Database; Computer security; Service (business); Business; Operating system; Microeconomics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006395338,0.0002311748,0.0003254925,0.0005640679,0.0001896723,0.0003032853,0.002215735,0.000299287,0.0003838761],"category_scores_gemma":[0.00004919772,0.0002205631,0.0001561393,0.002358598,0.0001271017,0.001124188,0.00003055143,0.0007877029,0.001742019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001480399,"about_ca_system_score_gemma":0.000179659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003592407,"about_ca_topic_score_gemma":0.00001645716,"domain_scores_codex":[0.9977769,0.0001100229,0.0007588338,0.000357006,0.0006308836,0.0003663661],"domain_scores_gemma":[0.9956067,0.0004953218,0.0002198195,0.003390677,0.000102845,0.0001846321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002799925,0.001744248,0.000139308,0.00005366523,0.001981136,0.000004161153,0.005309326,0.195838,0.0001170811,0.6870576,0.01597286,0.09150268],"study_design_scores_gemma":[0.001078229,0.000256246,0.00002803759,0.00003589367,0.0003024692,0.000007085922,0.0001672329,0.9864019,0.001578266,0.001115405,0.00867147,0.0003577335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0316158,0.000002071449,0.9630119,0.000801502,0.001381358,0.0005198352,0.0005990988,0.0002585612,0.001809908],"genre_scores_gemma":[0.9929478,0.00001317428,0.005826673,0.0006891087,0.000125101,0.00002273718,0.0002917539,0.0000111902,0.00007245743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.961332,"threshold_uncertainty_score":0.9990352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05415929401745084,"score_gpt":0.287022105062264,"score_spread":0.2328628110448132,"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."}}