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首页> 外文期刊>IEEE transactions on industrial informatics >Secure Storage Auditing With Efficient Key Updates for Cognitive Industrial IoT Environment
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Secure Storage Auditing With Efficient Key Updates for Cognitive Industrial IoT Environment

机译：安全存储审核，具有高效的认知工业物联网环境更新

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摘要

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.
机译：对大数据的认知计算为工业信息学的企业和组织带来了更多的开发机会，并且当他们面临数据安全挑战时可以为他们做出更好的决定。为了满足工业Internet（IOT）中实时数据存储的要求（IOT），远程无约束存储云通常用于存储生成的大数据。然而，云服务提供商的半峰的特征确定数据所有者担心存储在云计算中的数据是否已被损坏。在本文中，提出了一种安全的存储审核，它支持有效的关键更新，可以很好地用于认知工业物联网环境。此外，所提出的基本审计可以扩展到支持适用于多个终端设备的批量审计，以便在实践中同时审核其数据块。此外，提出了一种混合数据动态方法，该方法采用哈希表来存储数据块并使用链接列表来定位操作的数据块。与以前的方法相比，所提出的数据动态中的数据块位置时间可以减少40％。安全性分析结果表明，所提出的方案可以证明是正确的，并且在计算差别 - 地狱（CDH）和离散对数（DL）假设下是安全的。

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