Education Cloud Data Integrity Verification Based on Mapping-Trie Tree

Autor: Zhanfang Chen, Yuanshuai Wang, Kexin Wang, Zhangnan Yang
Rok vydání: 2019
Předmět:
Zdroj: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI).
DOI: 10.1109/mlbdbi48998.2019.00036
Popis: There are privacy data leakage risks and low verification efficiency against the existing public cloud data integrity audit model. Based on the characteristics of the education cloud, this paper designs an education cloud data integrity verification scheme based on the Mapping-Trie tree. Remove the Third Party Auditor platforms to prevent disclosure of private information. The method of random sampling is used to audit the data, which greatly saves the calculation and communication resources of users and servers. Both the Hash scheme and the Trie scheme have been improved in terms of audit efficiency and saves the space of storage.
Databáze: OpenAIRE