华东师范大学学报(自然科学版) ›› 2020, Vol. 2020 ›› Issue (5): 33-43.doi: 10.3969/j.issn.1000-5641.202091012

• 数据治理 • 上一篇    下一篇

云存储中基于MHT的电力设备图像完整性审计方案

张驯1, 白万荣1, 魏峰1, 王蓉1, 田秀霞2, 刘天顺2   

  1. 1. 国家电网甘肃省电力公司电力科学研究院, 兰州 730070;
    2. 上海电力大学 计算机科学与技术学院, 上海 200090
  • 收稿日期:2020-08-12 发布日期:2020-09-24
  • 通讯作者: 田秀霞,女,教授,研究方向为数据库安全、隐私保护、数字图像篡改检测、面向电力用户的安全计算、安全机器学习等.E-mail:xxtian@shiep.edu.cn E-mail:xxtian@shiep.edu.cn
  • 作者简介:张驯,男,高级工程师,研究方向为信息安全、电力无线网络安全、电力图像处理等.E-mail:zhangxunsf@qq.com
  • 基金资助:
    国家自然科学基金(61772327,61532021);国家电网甘肃省电力公司电力科学研究院横向项目(H2019-275)

An integrity auditing scheme based on MHT for power equipment images stored in the cloud

ZHANG Xun1, BAI Wanrong1, WEI Feng1, WANG Rong1, TIAN Xiuxia2, LIU Tianshun2   

  1. 1. State Grid Gansu Electric Power Company Electric Power Research Institute, Lanzhou 730070, China;
    2. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
  • Received:2020-08-12 Published:2020-09-24

摘要: 针对云存储中电力设备图像面临着被攻击、篡改或丢失等风险, 提出一种适用于云端电力设备图像的完整性审计方案. 首先, 将每个图像切割成4个图像块, 再采用尺度不变特征转换(Scale Invariant Feature Transform, SIFT)算法对所有图像块进行特征提取. 然后, 把每个图像的4个图像块作为一个叶子节点来构建Merkle哈希树(Merkle Hash Tree, MHT). 最后, 在树中节点增设访问等级位和更新状态位. 理论分析和实验结果表明, 该方案应用于图像完整性审计时具有较低的计算开销和较高的审计效率, 并且对图像的不完整区域能够进行准确的定位, 因此更加适用于云端电力设备图像的完整性审计工作.

关键词: 电力设备图像, 云存储安全, Merkle哈希树, SIFT, 图像完整性

Abstract: This paper proposes an integrity auditing scheme suitable for power equipment images stored in the cloud with the aim of addressing the risks of being attacked, tampered with, or lost. First, each image is cut into four image blocks, and then a Scale Invariant Feature Transform (SIFT) algorithm is used to extract features from the image blocks. The four image blocks for each image are subsequently used as a leaf node to construct a Merkle Hash Tree (MHT). Finally, access level bits and update status bits are added to the nodes of the tree. Theoretical analysis and experimental results show that the proposed image integrity auditing scheme has lower computational overhead and higher audit efficiency compared to existing approaches; hence, the scheme can accurately locate the incomplete area of an image and is suitable for auditing integrity of power equipment images in a cloud storage environment.

Key words: power equipment image, cloud storage security, Merkle Hash Tree, SIFT, image integrity

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