基于多混沌系统的双图像光学加密算法

为了扩展双图像光学加密算法的密钥空间,克服双随机相位加密系统中随机相位掩模作为密钥难于存储、传输和重构的问题,突破传统图像加密的研究思路,提出了一种基于多混沌系统的双图像加密算法,构造了光学加密系统。系统增加混沌系统参数作为密钥,利用混沌加密密钥空间大和图像置乱隐藏性好的特点,构建基于Logistic混沌映射的图像置乱算法,利用Kent混沌映射生成的伪随机序列构造出一对随机相位掩模,分别放置在分数傅里叶变换光学装置的两端,图像经加密系统变换后得到密文。数值仿真结果表明,算法的密钥敏感性极高,能够有效地对抗统计攻击,具有较高的安全性。     

一种基于混沌系统的新型图像加密算法

针对加密算法对明文敏感性较低的问题,提出了一种基于混沌系统的新型图像加密算法。采用SHA256函数对明文图像进行计算得到初值,将初值代入混沌系统生成伪随机序列,进一步使用该序列完成对明文图像的置乱。使用并行扩散机制对置乱图像进行第一轮扩散,在此基础上利用DNA动态编码技术进行第二轮扩散。对DNA矩阵解码得到二进制矩阵,进一步转化为十进制矩阵,得到加密图像。进行了计算机仿真分析和对比,通过对统计特性、差分特性、信息熵、密钥和鲁棒性的分析与测试,表明该算法安全性较好且易于实现,具有较大的应用前景。     

一种基于时滞混沌的加密算法

基于半导体激光时滞混沌映射,提出一种新的加密算法.用Ikeda方程产生的二进制序列掩盖明文,对明文块做依赖于密钥的置换,并用传统的混沌加密方法加密.在每一轮加密过程中,都会用一个与混沌映射、明文和密文相关的随机数对时滞项做微扰,以提高算法的安全性;状态转移函数不仅与密钥相关,而且与本轮输入的明文符号以及上一轮输出的密文符号相关,有效地防止了选择明文/密文攻击.仿真实验表明,该算法可行、有效.     

基于Hadoop大数据平台和无简并高维离散超混沌系统的加密算法

针对目前大数据环境中存在的数据安全问题,提出一种基于Hadoop大数据平台和无简并高维离散超混沌系统的加密算法.算法采用流密码对称加密方式,在Hadoop平台上读取存储于HDFS(Hadoop distributed file system)的大数据,进行分片处理和MapReduce编程后,用Map函数实现数据并行加密和解密,通过Reduce函数实现数据的合并操作并存储于HDFS.该算法具有较好的执行效率.与正李氏指数发生简并的低维混沌系统相比,无简并高维离散超混沌加密算法能提高系统安全性能,李氏指数均为正并且足够大,具有更好的统计特性,可通过严格的TESTU01测试,并行加密的密文之间互相关性很小.密钥参数众多使得估计或辨识难度增大.在密文闭环反馈条件下,具有抵御已知明文攻击和选择明文攻击的能力.     

基于深度学习压缩感知与复合混沌系统的通用图像加密算法

提出一种适用于灰度图像与RGB格式彩色图像的通用图像加密算法.利用双线性插值Bilinear与卷积神经网络对图像进行压缩,再使用二维云模型与Logistic组成的复合混沌系统对压缩图像加解密(滑动置乱与矢量分解),最后对解密图像进行重构.重构网络中,由卷积神经网络与双线性插值Bilinear主要负责重构轮廓信息,全连接层主要负责重构颜色信息.实验结果表明,该基于深度学习压缩感知与复合混沌系统的通用图像加密算法在数据处理质量和计算量上有着很大优势.由于复合混沌系统有着足够大的密钥空间且将明文哈希值与密钥关联,可实现一图一密的加密效果,能有效抵抗暴力攻击与选择明文攻击,与对比文献相比,相关系数更接近理想值且信息熵与明文敏感性指标也都在临界值范围内,其加密算法有着更高的安全性.     

基于无线传感器网络的混合混沌新分组加密算法

针对无线传感器网络(WSNS)中节点配备的能源少、节点计算能力低、存储资源 有限以及传统的加密方法不适用于WSNS中等问题, 提出了一种新的基于动态迭代的混合混沌方程及其整型数值化方法, 并结合Feistel网络结构设计了一种快速、安全且资源消耗低的适用于WSNS节点的分组加密算法. 通过对混合混沌分组加密算法进行了大量的实验测试之后, 发现该算法具有密钥空间大、严格的雪崩效应、扩散及扰乱性高以及均等的统计平衡性等优点, 同时该算法还成功地通过了SP800-22的严格测试; 算法经过仿真器平台上运行的速度、时间及所占存储空间的测试分析, 结果表明设计的混合混沌分组加密算法是完全能够适用于WSNS节点的数据加密.     

基于新的五维多环多翼超混沌系统的图像加密算法

本文提出了一种基于新的五维多环多翼超混沌系统的数字图像加密方法.首先,将明文图像矩阵和五条混沌序列分别通过QR分解法分解成一个正交矩阵和一个上三角矩阵,将混沌系统产生的五条混沌序列分别通过LU分解法分解成一个上三角矩阵和一个下三角矩阵,分别将两个上三角矩阵和一个下三角矩阵相加,得到五个离散后的混沌序列;其次,将明文图像矩阵分解出来的正交矩阵与五个混沌序列分解出来的五个正交矩阵相乘,同时把明文图像矩阵分解出来的上三角矩阵中的元素通过混沌序列进行位置乱,再将操作后的两个矩阵相乘;最后,将相乘后的矩阵通过混沌序列进行比特位位置乱,再用混沌序列与其进行按位“异或”运算,得到最终加密图像.理论分析和仿真实验结果表明该算法的密钥空间远大于10^200,密钥敏感性强,能够有效地抵御统计分析和灰度值分析的攻击,对数字图像的加密具有很好的加密效果.     

陈氏混沌系统的采样数据反馈控制

提出了基于采样数据反馈控制技术对陈氏混沌系统进行控制的理论.以给定的采样速率对陈氏混沌系统的输出进行采样.利用采样数据去构造离散时间控制算法,以达到将陈氏混沌系统控制到原点的目的.给出了被控制系统渐近稳定的理论证明,并给出了数值试验的结果. 关键词：     

基于混沌的改进双随机相位编码图像加密算法

针对菲涅耳域双随机相位编码提出的一种改进图像加密系统。该系统通过预先将原图像编码为相位信息克服了原算法对第一块相位模板和第一次衍射距离不敏感的缺陷。在双随机相位编码模块后基于复值图像振幅及相位替代的再次加密,使得加密图像像素值分布更为均匀。另外,改进算法引入三种不同的混沌系统来生成所需要的随机模板,借助于混沌系统的非线性性、初值敏感性,加密系统在减小密钥体积的同时增大了密钥空间、增加了系统的复杂性。仿真实验对算法进行了有效性分析、统计分析以及密钥敏感性测试,结果表明改进的算法有效提升了原算法的安全性。     

混沌参数调制下RSA数据加密算法研究

随着网络信息通讯技术的发展,信息数据的通讯安全成为信息化网络信息数据通讯过程中的重要问题。面对大量侵入程序与数据漏洞的威胁,一套强有效的数据加密算法成为数据研究领域的研究方向。RSA数据加密算法作为如今常用的安全性最高的算法,在大数据动态数据节点混沌排列的条件下,无法有效保证数据的加密安全。混沌参数下,出现加密逻辑断裂、溢出、数列逆排等严重的算法漏洞。对此,提出混沌参数调制下RSA数据加密算法研究,采用混沌参数特征处理单元、特征序列逻辑控制单元与混沌FIE-RSA算法,对传统RSA加密算法存在的问题进行针对性解决。通过仿真实验证明,提出的混沌参数调制下RSA数据加密算法研究中,采用的一系列方法具有加密处理响应速度快、处理运算时间短、加密安全度高、反破解性能强等特点。     

Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.     

基于Map-Reduce并行构架和AE的大数据指纹监控识别算法

传统的指纹识别监控方法在应用到以海量、在线和增量为特征的大数据环境中时,不能自动高效地进行识别,且识别率和识别精度都不高。由此设计了一种基于Map-Reduce并行框架和AE的大数据智能监控指纹识别算法。首先,描述了Hadoop和Map-Reduce实现并行计算的原理;然后,设计了基于AE和BP神经网络的识别模型,采用AE进行指纹图像自动特征提取,并提出了一种基于比较差异算法对AE进行参数初始化的训练算法,采用BP神经网络进行具体识别;最后,将识别过程分解为Map函数和Reduce函数,并对函数的具体功能进行了详细定义。在Map-Reduce并行框架下对FVC2004中的指纹数据库进行实验,实验结果表明了文中方法能自动、高效地进行指纹识别,与其他方法相比,具有识别效率高和识别精度高的优点,具有较大的优越性。     

A Bit Shift Image Encryption Algorithm Based on Double Chaotic Systems

A chaotic system refers to a deterministic system with seemingly random irregular motion, and its behavior is uncertain, unrepeatable, and unpredictable. In recent years, researchers have proposed various image encryption schemes based on a single low-dimensional or high-dimensional chaotic system, but many algorithms have problems such as low security. Therefore, designing a good chaotic system and encryption scheme is very important for encryption algorithms. This paper constructs a new double chaotic system based on tent mapping and logistic mapping. In order to verify the practicability and feasibility of the new chaotic system, a displacement image encryption algorithm based on the new chaotic system was subsequently proposed. This paper proposes a displacement image encryption algorithm based on the new chaotic system. The algorithm uses an improved new nonlinear feedback function to generate two random sequences, one of which is used to generate the index sequence, the other is used to generate the encryption matrix, and the index sequence is used to control the generation of the encryption matrix required for encryption. Then, the encryption matrix and the scrambling matrix are XORed to obtain the first encryption image. Finally, a bit-shift encryption method is adopted to prevent the harm caused by key leakage and to improve the security of the algorithm. Numerical experiments show that the key space of the algorithm is not only large, but also the key sensitivity is relatively high, and it has good resistance to various attacks. The analysis shows that this algorithm has certain competitive advantages compared with other encryption algorithms.     

A New Algorithm for Digital Image Encryption Based on Chaos Theory

In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme’s effectiveness and show that this technique is a suitable choice for actual image encryption.     

混沌加密的虹膜识别系统的安全性

提出了针对特征码的单向耦合映像格混沌加密系统,以及Arnold变换与单向耦合映像格混沌系统相结合对虹膜信息进行加密的复合混沌加密系统.利用Arnold变换置乱并混淆虹膜图像,通过镜像解决零点不变性问题,最后通过单向耦合映像格混沌系统实现加密.实验表明本算法密钥空间大,统计特性好,密钥敏感性强,加密速度快,平均加密时间70 ms.     

基于随机分数傅里叶变换的双图像加密算法

利用光学随机分数傅里叶变换设计了一种双图像加密算法,并给出了相应的光学实现.加密算法中,将两幅原始图像分别作为加密系统输入复函数的振幅和位相分布函数,利用随机分数傅里叶变换进行加密,所得复函数的振幅即为加密图像,而位相部分是变换的输出相位,随机位相作为加密算法的密码.在数值模拟中,二值文本图像和灰度图像分别被作为原始图像用于加密结果分析和加密安全测试,结果表明该加密算法具有很好的安全性.     

An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher

In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption. The parallel compressed sensing method is adopted to ensure the encryption and decryption efficiency. The ZUC stream cipher is used to sample the one-dimensional chaotic map to reduce the correlation between elements and improve the randomness of the chaotic sequence. The compressed sensing measurement matrix is constructed by using the sampled chaotic sequence to improve the image restoration effect. In order to reduce the block effect after the parallel compressed sensing operation, we also propose a method of a random block of images. Simulation analysis shows that the algorithm demonstrated better encryption and compression performance.     

Research on Color Image Encryption Algorithm Based on Bit-Plane and Chen Chaotic System

In response to the problems of high complexity and the large amount of operations of existing color image encryption algorithms, a low-complexity, low-operation color image encryption algorithm based on a combination of bit-plane and chaotic systems is proposed that is interrelated with plaintext information. Firstly, three channels of an RGB image are extracted, and the gray value of each pixel channel can be expressed by an eight-bit binary number. The higher- and lower-four bits of the binary gray value of each pixel are exchanged, and the position of each four-bit binary number is scrambled by a logistic chaotic sequence, and all the four-bit binary numbers are converted into hexadecimal numbers to reduce the computational complexity. Next, the position of the transformed image is scrambled by a logistic chaotic sequence. Then, the Chen chaos sequence is used to permute the gray pixel values of the permuted image. Finally, the gray value of the encrypted image is converted into a decimal number to form a single-channel encrypted image, and the three-channel encrypted image is synthesized into an encrypted color image. Through MATLAB simulation experiments, a security analysis of encryption effects in terms of a histogram, correlation, a differential attack, and information entropy is performed. The results show that the algorithm has a better encryption effect and is resistant to differential attacks.     

Parallel and Practical Approach of Efficient Image Chaotic Encryption Based on Message Passing Interface (MPI)

Encrypting pictures quickly and securely is required to secure image transmission over the internet and local networks. This may be accomplished by employing a chaotic scheme with ideal properties such as unpredictability and non-periodicity. However, practically every modern-day system is a real-time system, for which time is a critical aspect for achieving the availability of the encrypted picture at the proper moment. From there, we must improve encryption’s performance and efficiency. For these goals, we adopted the distributed parallel programming model, namely, the message passing interface (MPI), in this study. Using the message passing interface, we created a novel parallel crypto-system. The suggested approach outperforms other models by 1.5 times. The suggested parallel encryption technique is applicable.     

基于云计算的大数据自动分类处理系统设计

随着现代网络技术不断进步,系统数据量也在逐渐增多。传统的大数据自动分类处理系统已经无法满足现阶段用户需求,其软件与硬件的设计都比较单一,存在能源消耗大、分类速度慢、处理时间长、内存占用率高等问题,为此,提出基于云计算的大数据自动分类处理系统的设计。首先设计系统硬件结构,主要包括数据采集器、数据处理器以及数据自动存储模块,并详细的介绍了各硬件结构;然后利用时域特征提取数据的算法对频域特征数据进行提取,从而实现数据自动分类处理系统的软件设计;最后对两种系统性能进行对比实验。实验结果证明,基于云计算的大数据自动分类处理系统的资源不仅占用率低,内存消耗小,而且数据库内存较大。该系统不但可以提高数据自动分类精准度,还能加快数据分类速度,从而使系统拥有更好的分类性能。