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.
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.
[1] 姜哲. 无人机巡线电力设备故障的视觉检测技术 [D]. 西安: 电子科技大学, 2017.
[2] 何道敬, 杜晓, 乔银荣, 等. 无人机信息安全研究综述 [J]. 计算机学报, 2019, 42(5): 1076-1094
[3] 冯朝胜, 秦志光, 袁丁. 云数据安全存储技术 [J]. 计算机学报, 2015, 38(1): 150-163
[4] 冯登国, 张敏, 张妍, 等. 云计算安全研究 [J]. 软件学报, 2011, 22(1): 71-83
[5] WAZID M, DAS A K, KUMAR N, et al. Secure three-factor user authentication scheme for renewable-energy-based smart grid environment [J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 3144-3153.
[6] 谭霜, 贾焰, 韩伟红. 云存储中的数据完整性证明研究及进展 [J]. 计算机学报, 2015, 38(1): 164-177
[7] XU J, WEI L, ZhANG Y, et al. Dynamic fully homomorphic encryption-based merkle tree for lightweight streaming authenticated data structures [J]. Journal of Network and Computer Applications, 2018, 107: 113-124.
[8] PENG S, ZHOU F, LI J, et al. Efficient, dynamic and identity-based remote data integrity checking for multiple replicas [J]. Journal of Network and Computer Applications, 2019, 134: 72-88.
[9] SEBÉ F, DOMINGO-FERRER J, MARTINEZ-BALLESTE A, et al. Efficient remote data possession checking in critical information infrastructures [J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(8): 1034-1038.
[10] WANG Q, WANG C, REN K, et al. Enabling public auditability and data dynamics for storage security in cloud computing [J]. IEEE Transactions on Parallel and Distributed Systems, 2010, 22(5): 847-859.
[11] 张庆功, 赵现昌. SIFT算法在无人机遥感影像特征提取中的应用 [J]. 河南水利与南水北调, 2019, 48(11): 63-65
[12] 郭会, 邓伦治. 一个基于双线性映射的无证书多接收者签密方案 [J]. 贵州师范大学学报(自然科学版), 2018, 36(6): 98-105
[13] 许钟华, 张龙军. 基于MHT的云环境下数据完整性检验方案 [J]. 计算机与网络, 2014, 40(19): 63-66
[14] 苏迪, 刘竹松. 一种新型的Merkle哈希树云数据完整性审计方案 [J]. 计算机工程与应用, 2018, 54(1): 70-76.
[15] LI L M, YANG Y H, WU Z H. FMR-PDP: Flexible multiple replica provable data possession in cloud storage [C]//International Symposium on Computers and Communications. 2017: 1115-1121.
[16] 李晨. 云存储数据的安全审计技术研究 [D]. 西安: 电子科技大学, 2018.
[17] 秦志光, 王士雨, 赵洋, 等. 云存储服务的动态数据完整性审计方案[J]. 计算机研究与发展, 2015, 52(10): 2192-2199.
[18] PAPAMANTHOU C, TAMASSIA R, TRIANDOPOULOS N. Optimal authenticated data structures with multilinear forms [C]//International Conference on Pairing Based Cryptography. Springer, 2010: 246-264.
[19] HARIHARASITARAMAN S, BALAKANNAN S P. A dynamic data security mechanism based on position aware merkle tree for health rehabilitation services over cloud [J]. Journal of Ambient Intelligence and Humanized Computing, 2019(7): 1-15.
[20] ERWAY C, PAPAMANTHOU A C, TAMASSIA R. Dynamic provable data possession [J]. ACM Transactions on Information and System Security, 2015, 7(4): 1-29.
中国综合性科技类核心期刊(北大核心)