结合半张量积压缩感知的可验证图像加密

目的物联网(internet of things, IoT)感知层获取数据时存在资源受限的约束,同时数据常常遭受泄露和非法篡改。数据一旦遭到破坏,将对接收者造成很大的影响,甚至可能会比没有收到数据更加严重。针对IoT数据获取面临的能耗和安全问题,提出一种基于半张量积压缩感知的可验证图像加密方法。方法首先采用级联混沌系统生成测量矩阵和验证矩阵,测量矩阵以半张量积压缩感知的方式进行采样得到观测值矩阵。利用Arnold置乱观测值矩阵得到最终密文信号,与此同时由验证矩阵生成消息验证码一同在公共信道传输,将由级联混沌系统生成的测量矩阵、验证矩阵以及Arnold置乱的参数的初始种子作为密钥在安全信道上传输。结果密钥空间分析、密钥敏感性分析、图像熵分析、直方图分析、相关性分析、身份验证分析、压缩率分析的实验结果显示:相比于两种对比方法,本文算法加密后图像的熵值更接近于8,而对应密文图像像素之间的相关系数更接近于0。结论本文的可验证加密算法结合了半张量压缩感知的优点,在有效减少数据采样能耗的同时保证了数据在传输过程中的安全性与完整性。

Semi-tensor product compression sensing integrated to verifiable image encryption method

Objective Big multimedia data is acquired via various multimedia sensors and mobile devices nowadays. It is necessary to implement low-cost sampling compression coding due to the limited computing resources and large data volume of sensors and mobile devices.. Illegal applications from extracting valuable information is to be prevented during sampling and transmission. Compressed sensing has credited for data collection in the internet of things(IoT). As a novel signal acquisition theory, compressed sensing, has been focused on. The compressed sensing framework is a sort of encryption scheme. Compared with conventional encryption schemes, compressed sensing encryption schemes have their advantages, such as low computational cost of encryption process, synchronized realization of encryption and compression, and robustness of ciphertext. The compressed sensing framework for information authority will be concentrated. A way for the recipient has been confirmed the integrity of the information for information tampers. The emerging verification code is to check whether the content of the message has been changed in the process of message delivery, regardless of the accidental or deliberate attack change. The identity verification of the message source is to confirm the source of the message. A sequence value of a certain length is first obtained via the initial compressed message. The sequence value of the same length is generated for the verified message again in accordance with the one mapping method. The initial sequence to get the results have been compared with incomplete data. But, conventional methods are ineffective due to the avalanche effect of compressed sensing. In the compressed sensing framework, the measured value is transmitted instead of the original signal. The receiving end receives the measured value, and the original signal needs to be restored with a restoration algorithm. Compressed sensing can only make the restored signal approximate to the original signal. The message verification sequence generated by the receiving end is completely different from the received verification sequence. In the IoT perception layer, there are some constraints in data acquisition resource and suffer from privacy leakage and illegal tampering. To resolve energy consumption and security in IoT data acquisition, a verifiable image encryption method has been illustrated based on semi-tensor product compression sensing.Method First, The measurement matrix and the verification matrix based on the cascade chaotic system are used to sample the sparse signal in terms of semi-tensor product application. The measured value matrix is further used for Arnold scrambling to obtain the final secret image. Simultaneously, the identity verification code is generated by the identity verification matrix and transmitted on the public channel, and the initial seed of the cascade chaotic system is as the key for transmission on the secure channel.Result Key space analysis, key sensitivity analysis, image entropy analysis, histogram analysis, correlation analysis, identity verification analysis, compression rate analysis have been tested and analyzed overall. The demonstrated results show that the encrypted image entropy in the scheme illustrated is closer to 8, while the encrypted image correlation coefficient is close to 0.Conclusion The verifiable encryption algorithm has integrated the advantages of semi-tensor compression perception. The security and integrity of data transmission has been realized effectively in terms of the decrease of energy consumption of data sampling.