Research On The Design And Application Of Image Encryption Algorithm Based On Compressed Sensing

With the widespread application of Internet technology,the image encryption technology needs to be completed,which is to ensure the security of image information.However,Compared to text,images are provided with inherent characteristics,including large amount of image data,high redundancy,and strong correlation between adjacent pixels,hence the traditional encryption algorithms,such as data encryption standard(DES)and advanced encryption standard(AES),which are no longer suitable for image encryption.Firstly,Based on compressed sensing theory,this paper proposes a compressiondiffusion permutation strategy,which can ensure the confidentiality of image data during transmission.Secondly,Privacy data is very important,hence,we store it in a private cloud.As the amount of data increases dramatically,how to securely store and share large image data from the Internet of Things has attracted increasingly attention.This thesis uses a hybrid cloud to implement secure image data services,saving a lot of space for private clouds.Finally,we conclude that edge intelligent industrial Internet of Things is confronted with several significant challenges,such security privacy and energy consumption.Therefore,we propose potential solutions based on compressive sensing in edge intelligent IIo T,including compressive sensing to achieve low-energy data acquisition,chaotic compression sensing to complete confidentiality-assured data acquisition,local disturbance with measurement matrix to decrease energy consumption,data authentication to identify tamper-proof mechanism,and access passwords to accomplish privacy enhancement.(1)A compression-diffusion-permutation strategy based on compressive sensing is proposed.Firstly,an image is sampled and compressed by using compressive sensing technique to save energy consumption in transmission,in which the measurement matrix is constructed through iterating two-dimensional Sine-Logistic modulation map(2DSLMM).XOR is used to further diffuse the compressed image to change the statistical properties of the image.Finally,the permutation is utilized to rearrange the positions of the diffused image,which can improve the confidentiality of the image data.Theoretical analysis and experimental results verify the security and efficiency of the algorithm.(2)We harness the hybrid cloud to provide secure big image data storage and share service for users.Firstly,the image is divided into a small group of sensitive image data and a large group of insensitive image data based on sensitivity identification approaches like Sobel edge detector,in which the sensitive image data and insensitive image data rely on the users' interests and requirements.Secondly,different encryption algorithms are used for the obtained sensitive image data and the insensitive image data.Because sensitive image data is very important to users,the sensitive image data are encrypted in parallel in the similar way of the counter mode and store in private Cloud,which can ensure the confidentiality of the sensitive image data.The insensitive image data are encrypted with the architecture of permutation-diffusion and then subsampled by parallel compressed sampling technique and store in public Cloud,which is to save the storage space of the private Cloud.Experimental results demonstrate that the proposed framework can provide secure big image data service.(3)We state the challenges in edge intelligent industrial Internet of Thing,such as security privacy and energy consumption.we propose potential solution in accordance with the challenges,including using compressive sensing technology to achieve low-energy data acquisition,employing chaotic compression sensing to complete confidentialityassured data acquisition,utilizing local disturbance with measurement matrix to achieve energy consumption decrease,adopting data authentication to identify tampering mechanism,and applying access passwords to accomplish privacy enhancement.The experimental results verify the feasibility of the solutions.