面向雾计算的压缩感知图像秘密传输和篡改恢复方案

doi:10.3969/j.issn.1671-1122.2021.04.002

摘要/Abstract

摘要：

在雾计算三层数据传输框架下,用户的图像数据隐私泄露依然是最严重的问题之一,特别是大多数加密方法都不适用于物理终端资源受限的情况。因此,针对这类场景,需要设计合适的数据隐私保护方法。压缩感知具有轻量级和保密性,可以解决该问题。文章提出一种基于压缩感知的图像数据三层传输方案,不仅可以实现数据的安全传输,还可以结合里德-所罗门码（RS）应对篡改攻击,实现篡改定位和恢复。保密性方面,终端利用块压缩感知实现轻量级加密。由于测量值可能泄露明文能量,在雾服务器上将测量值进行局部归一化,对产生的能量值进行高强度加密,使传输过程达到完美保密。完整性方面,利用压缩感知的鲁棒性和RS编码的冗余性实现数据的篡改定位和双重篡改恢复。仿真结果和分析验证了该方案的性能。

关键词: 雾计算, 压缩感知, 图像加密, 篡改恢复

Abstract:

In the three-layer data transmission framework of fog computing, the image data privacy leakage is still one of the most serious problems. In particular, most of the encryption methods are not suitable for the situation of limited physical terminal resources. Therefore, for these scenarios, we need to design an appropriate method to protect data privacy. Compressed sensing(CS) is promising to solve this problem because of its lightweight and confidentiality. In this paper, a three-layer transmission scheme of image data based on CS encryption algorithm is proposed: it can not only realize the secure transmission of data, but also realize the tamper location and recovery by combining with Reed-solomon code(RS) to deal with tamper attacks. In the aspect of confidentiality, the block CS is used to achieve lightweight encryption on the terminal, then the measured value is locally normalized in the fog server due to that the measured values may leak the plaintext energy, and then the generated energy values is encrypted with high intensity so that the transmission process can achieve perfect confidentiality. In the aspect of integrity, the robustness of CS and the redundancy of RS coding are used to achieve the location and recovery of tampered data. Simulation results and analyses verify the performance of the proposed scheme.

Key words: fog computing, compressed sensing, image encryption, tamper recovery

中图分类号:

TP309

郑洪英, 李琳, 肖迪. 面向雾计算的压缩感知图像秘密传输和篡改恢复方案[J]. 信息网络安全, 2021, 21(4): 10-20.

ZHENG Hongying, LI Lin, XIAO Di. Secret Image Transmission and Tamper Recovery Based on Compressed Sensing for Fog Computing[J]. Netinfo Security, 2021, 21(4): 10-20.

图/表 13

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

图10

表2

图11

参考文献 22

[1]	MELL P M, GRANCE T. The NIST Definition of Cloud Computing[EB/OL]. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf, 2020-07-12.
[2]	TAYADE D. Mobile Cloud Computing: Issues, Security, Advantages, Trends[J]. International Journal of Computer Science and Information Technologies, 2014,5(5):6635-6639.
[3]	BONOMI F, MILITO R, ZHU Jiang, et al. Fog Computing and Its Role in the Internet of Things[C]// ACM. The First Edition of the MCC Workshop on Mobile Cloud Computing, August 17, 2012, Helsinki, Finland. New York: ACM, 2012: 13-16.
[4]	WANG Tian, ZHOU Jiyuan, CHEN Xinlei, et al. A Three-layer Privacy Preserving Cloud Storage Scheme Based on Computational Intelligence in Fog Computing[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018,2(1):3-12. doi: 10.1109/TETCI URL
[5]	WANG Tian, MEI Yaxin, JIA Weijia, et al. Edge-based Differential Privacy Computing for Sensor-cloud Systems[EB/OL]. https://www.sciencedirect.com/science/article/abs/pii/S074373151930293X, 2020-07-14.
[6]	DONOHO D L. Compressed Sensing[J]. IEEE Transactions on Information Theory, 2006,52(4):1289-1306. doi: 10.1109/TIT.2006.871582 URL
[7]	GAN Lu. Block Compressed Sensing of Natural Images[C]// IEEE. 15th International Conference on Digital Signal Processing, August 1-4, 2007, Cardiff, UK. New Jersey: IEEE, 2007: 403-406.
[8]	BARANIUK R, DAVENPORT M, DEVORE R, et al. A Simple Proof of the Restricted Isometry Property for Random Matrices[J]. Constructive Approximation, 2008,28(3):253-263. doi: 10.1007/s00365-007-9003-x URL
[9]	CANDES E J, TAO T. Near-optimal Signal Recovery from Random Projections: Universal Encoding Strategies?[J]. IEEE Transactions on Information Theory, 2006,52(12):5406-5425. doi: 10.1109/TIT.2006.885507 URL
[10]	BIANCHI T, BIOGLIO V, MAGLI E, et al. Analysis of One-time Random Projections for Privacy Preserving Compressed Sensing[J]. IEEE Transactions on Information Forensics and Security, 2016,11(2):313-327. doi: 10.1109/TIFS.2015.2493982 URL
[11]	CHEN Junxin, ZHANG Yu, LIN Qi, et al. Exploiting Chaos-based Compressed Sensing and Cryptographic Algorithm for Image Encryption and Compression[J]. Optics & Laser Technology, 2018,99(1):238-248.
[12]	ZHOU Kanglei, FAN Jingjing, FAN Haiju, et al. Secure Image Encryption Scheme Using Double Random-phase Encoding and Compressed Sensing[EB/OL]. https://www.sciencedirect.com/science/article/abs/pii/S0030399219309648, 2020-08-12.
[13]	CHAI Xiuli, FU Xianglong, GAN Zhihua, et al. An Efficient Chaos-based Image Compression and Encryption Scheme Using Block Compressive Sensing and Elementary Cellular Automata[EB/OL]. https://link.springer.com/article/10.1007/s00521-018-3913-3#article-info, 2018-12-29.
[14]	LIU Zhanqiang, WANG Licheng, WANG Xianmin, et al. Secure Remote Sensing Image Registration Based on Compressed Sensing in Cloud Setting[EB/OL]. https://ieeexplore.ieee.org/document/8663267, 2019-03-08.
[15]	XIE Dong, CHEN Fulong, LUO Yonglong, et al. One-to-many Image Encryption with Privacy-preserving Homomorphic Outsourced Decryption Based on Compressed Sensing[EB/OL]. https://www.sciencedirect.com/science/article/abs/pii/S1051200419301332, 2020-08-20.
[16]	ZHANG Yushu, XIANG Yong, ZHANG L Y, et al. Efficiently and Securely Outsourcing Compressed Sensing Reconstruction to a Cloud[EB/OL]. https://www.sciencedirect.com/science/article/abs/pii/S0020025519304293, 2020-08-20.
[17]	ZHANG Yushu, WANG Ping, FANG Liming, et al. Secure Transmission of Compressed Sampling Data Using Edge Clouds[J]. IEEE Transactions on Industrial Informatics, 2020,16(10):6641-6651. doi: 10.1109/TII.9424 URL
[18]	KANG Liwei, LU Chunshien, HSU C Y, et al. Compressive Sensing-based Image Hashing[C]// IEEE. 16th IEEE International Conference on Image Processing, November 7-10, 2009, Cairo, Egypt. New Jersey: IEEE, 2009: 1285-1288.
[19]	WU T, RULAND C. An Improved Authenticated Compressive Sensing Imaging[C]// IEEE. 12th International Conference on Semantic Computing, January 31-February 2, 2018, Laguna Hills, CA, USA. New Jersey: IEEE, 2018: 164-171.
[20]	BIANCHI T, BIOGLIO V, MAGLI E. On the Security of Random Linear Measurements[C]// IEEE. IEEE International Conference on Acoustics, Speech and Signal Processing, May 4-9, 2014, Florence, Italy. New Jersey: IEEE, 2014: 3992-3996.
[21]	PLANK J S. T1: Erasure Codes for Storage Applications[EB/OL]. http://ecee.colorado.edu/ecen5653/ecen5653/papers/Erasure-Code-Overview-FAST-2005.pdf, 2020-09-10.
[22]	PAK C, HUANG L. A New Color Image Encryption Using Combination of the 1D Chaotic Map[EB/OL]. https://dl.acm.org/doi/abs/10.5555/3085606.3085775, 2020-09-14.

篡改数量		未篡改	1	2	5	10	20
文献[17]算法		39.1742	39.0726	38.9854	38.4055	37.9115	35.7505
本文算法	r=2	39.1742	39.1742	39.1742	39.1742	39.1742	36.2346
本文算法	r=3	39.1742	39.1742	39.1742	39.1742	39.1742	37.3796

污染噪声	失真率/%	验证情况	PSNR	验证结果
未污染	0.70	通过	39.1742	成功
正常高斯白噪声	1.90	通过	32.7428	成功
重度高斯白噪声	9.35	未通过	27.7479	成功
泊松噪声	2.15	通过	31.1069	成功