云存储中数据完整性自适应审计方法

发布时间：2018-08-17 16:55

【摘要】：作为云存储安全的重要问题,数据完整性验证技术受到学术界和工业界的广泛关注.为了验证云端数据完整性,研究者提出了多个数据完整性公开审计模型.然而,现有的数据完整性审计模型采用固定参数审计所有文件,浪费了大量计算资源,导致系统审计效率不高.为了提高系统的审计效率,提出了一种自适应数据持有性证明方法(self-adaptive provable data possession,SA-PDP),该方法基于文件属性和用户需求动态调整文件的审计方案,使得文件的审计需求和审计方案的执行强度高度匹配.为了增强审计方案更新的灵活性,依据不同的审计需求发起者,设计了2种审计方案动态更新算法.主动更新算法保证了审计系统的覆盖率,而被动更新算法能够及时满足文件的审计需求.实验结果表明:相较于传统方法,SA-PDP的审计总执行时间至少减少了50%,有效增加了系统审计文件的数量.此外,SAPDP方法生成的审计方案的达标率比传统审计方法提高了30%.
[Abstract]:As an important issue of cloud storage security, data integrity verification technology has been widely concerned by academia and industry. To verify cloud data integrity, researchers proposed several open audit models for data integrity. However, the existing data integrity audit model uses fixed parameters to audit all files, which wastes a lot of computing resources and leads to inefficient system audit. In order to improve the audit efficiency of the system, an adaptive self-adaptive provable data possessionSA-PDP (self-adaptive provable data possessionSA-PDP) is proposed, which dynamically adjusts the audit scheme based on file attributes and user requirements. The audit requirements of the document are highly matched with the execution intensity of the audit scheme. In order to enhance the flexibility of audit scheme updating, two dynamic updating algorithms are designed according to different audit requirements. The active updating algorithm ensures the coverage of the audit system, while the passive updating algorithm can meet the audit requirements in time. The experimental results show that the total audit execution time of SA-PDP is at least 50 less than that of the traditional method, and the number of audit files is increased effectively. In addition, the SAPDP method has improved the audit program's compliance rate by 30% compared with the traditional audit method.
【作者单位】：西北工业大学计算机学院;
【基金】：国家“八六三”高技术研究发展计划基金项目(2013AA01A215) 国家自然科学基金项目(61472323,61502392) 中央高校基本科研业务费专项资金项目(3102015JSJ0009) 华为创新基金项目(YB2014040023)~~
【分类号】：TP309;TP333

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