Plaintext Attack Research Articles

QMedShield: a novel quantum chaos-based image encryption scheme for secure medical image storage in the cloud

In the age of digital technology, medical images play an important role in the healthcare industry, which aids surgeons in making precise decisions and reducing the diagnosis time. However, the storage of large amounts of these images in third-party cloud services raises privacy and security concerns. There are a lot of classical security mechanisms to protect them. Although, the advent of quantum computing entails the development of quantum-based encryption models for healthcare. There is a significant demand for resource-efficient, quantum-secure image encryption mechanisms that leverage existing classical infrastructure. Hence, in this paper, a novel quantum chaos-based encryption scheme for medical images has been introduced. The model utilizes bit-plane scrambling, a 3D quantum logistic map, quantum operations in the diffusion phase, a hybrid chaotic map, and DNA encoding in the confusion phase to transform the plain medical image into a cipher medical image. The proposed scheme has been evaluated using multiple statistical analyses and validated against more attacks such as differential attacks, known and chosen plaintext attacks with three different medical datasets, and theoretically, as well. Hence, the introduced encryption model has proved to be more attack-resistant and robust than other existing image encryption schemes, ensuring the secure storage of medical images in cloud environments.

A counter mode and multi-channel based chaotic image encryption algorithm for the internet of things

To deal with the threat of image privacy leakage in the Internet of things, this paper presents a novel batch images encryption algorithm using the counter mode and a multi-channel processing scheme. We employ multi-thread technique combined with an adapter to construct a novel multi-channel processing scheme, which can encrypt four different sized images in one round. Moreover, the counter encryption mode, which can compute round keys from a plaintext related session key, is introduced to decrease the difficulty of session key management when dealing with batch images. The security tests demonstrate the exceptional performance of the proposed algorithm in terms of security, as evidenced by P-values of statistical tests far larger than 0.01, correlation coefficients and entropies of cipher images close to 0 and greater than 7.99. Additionally, the results of NPCR and UACI tests closely approximate the theoretical values 99.6094% and 33.4635%, the proposed algorithm can better resist statistical, exhaustive, differential, or even chosen plaintext attacks. Moreover, due to the novel parallel scheme with a linear time complexity of O(2W+2H), which demonstrates an acceleration of over 300% compared to existing algorithms, it only takes 2.1sto encrypt one hundred images with varying sizes. Therefore, the proposed algorithm succeeds in exceeding existing algorithms in meeting the efficiency and security requirements for encrypting batch images.

One Class of Ideally Secret Autonomous Symmetric Ciphering Systems Based on Wiretap Polar Codes

This paper introduces a class of symmetric ciphering systems with a finite secret key, which provides ideal secrecy, autonomy in key generation and distribution, and robustness against the probabilistic structure of messages (the Ideally Secret Autonomous Robust (ISAR) system). The ISAR system is based on wiretap polar codes constructed over an artificial wiretap channel with a maximum secrecy capacity of 0.5. The system autonomously maintains a minimum level of key equivocation by continuously refreshing secret keys without additional key generation and distribution infrastructure. Moreover, it can transform any stream ciphering system with a finite secret key of known length into an ISAR system without knowing and/or changing its algorithm. Therefore, this class of system strongly supports privacy, a critical requirement for contemporary security systems. The ISAR system’s reliance on wiretap polar coding for strong secrecy ensures resistance to passive known plaintext attacks. Furthermore, resistance to passive attacks on generated refreshing keys follows directly from ideal secrecy and autonomy. The results presented offer an efficient methodology for synthesizing this class of systems with predetermined security margins and a complexity of the order of nlog⁡n, where n is the block length of the applied polar code.

Drone-Captured Wildlife Data Encryption: A Hybrid 1D–2D Memory Cellular Automata Scheme with Chaotic Mapping and SHA-256

In contemporary wildlife conservation, drones have become essential for the non-invasive monitoring of animal populations and habitats. However, the sensitive data captured by drones, including images and videos, require robust encryption to prevent unauthorized access and exploitation. This paper presents a novel encryption algorithm designed specifically for safeguarding wildlife data. The proposed approach integrates one-dimensional and two-dimensional memory cellular automata (1D MCA and 2D MCA) with a bitwise XOR operation as an intermediate confusion layer. The 2D MCA, guided by chaotic rules from the sine-exponential (SE) map, utilizes varying neighbor configurations to enhance both diffusion and confusion, making the encryption more resilient to attacks. A final layer of 1D MCA, controlled by pseudo-random number generators, ensures comprehensive diffusion and confusion across the image. The SHA-256 hash of the input image is used to derive encryption parameters, providing resistance against plaintext attacks. Extensive performance evaluations demonstrate the effectiveness of the proposed scheme, which balances security and complexity while outperforming existing algorithms.

A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

Lightweight distributed deep learning on compressive measurements for internet of things

In this work, we investigate the problem of distributed deep learning in Internet of Things (IoT). The proposed learning framework is constructed in a fog–cloud computing architecture, so as to overcome the limitation of resource constrained IoT end device. Compressive Sensing (CS) is used as a lightweight encryption in the framework to preserve the privacy of training data. Specifically, a chaotic-based CS measurement matrix construction mechanism is applied in the system to save the storage and transmission costs. With this design, the computation overhead of the learning framework in IoT can be successfully offloaded from IoT end device to the fog nodes. Theoretical analysis demonstrates that our system can guarantee security of the raw data against chosen plaintext attack (CPA). Experimental and analysis results show that our privacy-preserving proposal can significantly reduce the communication costs and computation costs with only a negligible accuracy penalty (with classification accuracy 91% testing on MNIST dataset under compression rate 0.5) compared to traditional non-private federated learning schemes. Notably, due to the chaotic-based CS measurement matrix construction mechanism, the memory requirement of end device side can be significantly reduced. This makes our framework be very suitable for the IoT applications in which end devices are equipped with low-spec chips.

Concurrent compression and meaningful encryption of images using chaotic compressive sensing

The presented research introduces a new approach to simultaneously compressing and encrypting images using chaotic compressive sensing. This technique involves transforming the image into sparser data using the discrete cosine transform basis, which is then compressed through projection onto a lower dimensional space using a measurement matrix designed based on a new chaotic map. The proposed chaotic map produced a Lyapunov exponent value of 2.675 proving its chaotic behavior. The proposed map is also highly sensitive to initial values, making it a secure basis for encryption. The compressed data with the proposed map is then embedded onto a colorful image for transmission. This approach achieves both compression and visually meaningful encryption of images. Quantitative and Qualitative results on the proposed compression-encryption algorithm shows the effectiveness of the methodology against chosen plaintext attacks and cipher-only attacks.

General secure encryption algorithm for separable reversible data hiding in encrypted domain

The separable reversible data hiding in encrypted domain (RDH-ED) algorithm leaves out the embedding space for the information before or after encryption and makes the operation of extracting the information and restoring the image not interfere with each other. The encryption method employed not only affects the embedding space of the information and separability, but is more crucial for ensuring security. However, the commonly used XOR, scram-bling or combination methods fall short in security, especially against known plaintext attack (KPA). Therefore, in order to improve the security of RDH-ED and be widely applicable, this paper proposes a high-security RDH-ED encryption algorithm that can be used to reserve space before encryption (RSBE) and free space after encryption (FSAE). During encryption, the image undergoes block XOR, global intra-block bit-plane scrambling (GIBS) and inter-block scrambling sequentially. The GIBS key is created through chaotic mapping transformation. Subsequently, two RDH-ED algorithms based on this encryption are proposed. Experimental results indicate that the algorithm outlined in this paper maintains consistent key communication traffic post key conversion. Additionally, its computational complexity remains at a constant level, satisfying separability criteria, and is suitable for both RSBE and FSAE methods. Simultaneously, while satisfying the security of a single encryption technique, we have expanded the key space to 28Np×Np!×8!Np, enabling resilience against various existing attack methods. Notably, particularly in KPA testing scenarios, the average decryption success rate is a mere 0.0067% and 0.0045%, highlighting its exceptional security. Overall, this virtually unbreakable system significantly enhances image security while preserving an appropriate embedding capacity.

Block-based color image encryption algorithm by a novel memristor chaotic system and new RNA computation

The security of images is closely related to the protection of information privacy. We proposed a novel 5D memory resistive chaotic system (5D-MRCS), which exhibits good chaotic characteristics. Therefore, we employed it to design an image encryption algorithm aimed at ensuring secure image transmission. To further enhance the complexity of the algorithm and obtain more chaotic sequences, we combine the 5D-MRCS with the Hodgkin-Huxley (HH) model and use this combination in algorithm design. Initially, we combine the plain image with the hash function SHA-384 to devise and generate the secret key. Subsequently, the algorithm determines whether to pad the plain image based on different block size requirements. Then, we use multiple chaotic sequences generated by the 5D-MRCS and HH model to perform the global image permutation operation. Our designed permutation algorithm includes two parts: Block-based permutation and a new pixel-level permutation. Next, the scrambled image undergoes block-based random RNA diffusion, incorporating two newly proposed methods in the RNA operations, ultimately resulting in the ciphertext image. The algorithm’s NPCR, UACI, information entropy, and other security performance metrics are very close to the ideal values, and it possess characteristics such as resistance to differential, cutting, chosen plaintext, and noise attacks. Compared with other algorithms, it still has some advantages across multiple images and demonstrates excellent image encryption performance.

Chosen Plaintext Attack on Single Pixel Imaging Encryption via Neural Differential Cryptanalysis

AbstractSingle pixel imaging (SPI) shows great potential in encryption by its indirect imaging mechanism. However, there appears to be room for further exploration in the corresponding cryptanalysis. Current studies primarily rely on straightforward end‐to‐end cryptanalysis of plain‐ciphertext pairs, ignoring the fundamental SPI optical path. As a result, the effectiveness of most attacks depends on the training data and the design of network, triggering low certainty and confidence. In this study, an alternative model is proposed to attack multiple SPI encrypting methods based on chosen plaintext attack framework, where arbitrary plaintexts can be encrypted as ciphertexts for cryptanalysis. In terms of the basic SPI setup, it is found that no matter how complicated the patterns are encrypted, the linear relationship between encrypted patterns and intensity always maintain. Thus, specifically, the ciphertext is first differentialized to derive encrypted patterns. By further reconstructing the pixel correlation of these derived patterns, deep learning is employed to correct them. Ultimately, the cracked patterns are used to decrypt plaintexts by conventional correlation. The experiments demonstrate that this method possesses a certain degree of reusability in the SPI encryption with linear propagating characteristic, like pattern‐encrypting class, demonstrating potential for the indirect optical encryption.

Secure collaborative EHR Sharing using multi-authority attribute-based proxy re-encryption in Web 3.0

Web 3.0 represents a transformative shift toward a decentralized, intelligent, and user-centric Internet. Existing electronic health record (EHR) sharing systems depend on centralized cloud servers for storage and management, with hospitals serving as primary custodians. This centralization often results in patients losing control and visibility over their EHR data, including who accesses it and how it is utilized, which contradicts the decentralized principles of Web 3.0. In this context, we propose a multi-authority attribute-based proxy re-encryption scheme that facilitates collaborative EHR sharing in Web 3.0. Our design allows the updating of ciphertext policies, thereby eliminating the need for frequent re-encryption of plaintext data amid varying cross-domain access policies. Furthermore, our scheme utilizes blockchain technology to create a decentralized and transparent environment that enables traceable cross-domain EHR sharing records. Additionally, we integrate hybrid encryption with decentralized data hosting platforms, significantly reducing the on-chain storage burden. The use of smart contracts automates the cross-domain EHR sharing and guarantees a fair distribution of benefits among all participants. Security analysis confirms that our scheme is secure against chosen plaintext attacks and resistant to collusion. Performance analysis and simulation experiments validate the efficiency and robustness of our scheme.

The Perils of Limited Key Reuse: Adaptive and Parallel Mismatch Attacks with Post-processing Against Kyber

The Module Learning With Errors (MLWE)-based Key Encapsulation Mechanism (KEM) Kyber is NIST's new standard scheme for post-quantum encryption. As a building block, Kyber uses a Chosen Plaintext Attack (CPA)-secure Public Key Encryption (PKE) scheme, referred to as Kyber.CPAPKE. In this paper we study the robustness of Kyber.CPAPKE against key mismatch attacks. We demonstrate that Kyber's security levels can be compromised if having access to a few mismatch queries of Kyber.CPAPKE, by striking a balance between the parallelization level and the cost of lattice reduction for post-processing. This highlights the imperative need to strictly prohibit key reuse in Kyber.CPAPKE. We further propose an adaptive method to enhance parallel mismatch attacks, initially proposed by Shao et al. at AsiaCCS 2024, thereby significantly reducing query complexity. This method combines the adaptive attack with post-processing via lattice reduction to retrieve the final secret key entries. Our method proves its efficacy by reducing query complexity by 14.6 % for Kyber512 and 7.5 % for Kyber768/Kyber1024. Furthermore, this approach has the potential to improve multi-value Plaintext-Checking (PC) oracle-based side-channel attacks and fault-injection attacks against Kyber itself.

A Blockchain‐Based Proxy Re‐Encryption Scheme With Cryptographic Reverse Firewall for IoV

ABSTRACTAs the internet of vehicles (IoV) technology develops, it promotes the intelligent interaction among vehicles, roadside units, and the environment. Nevertheless, it also brings vehicle information security challenges. In recent years, vehicle data sharing is suffering to algorithm substitution attacks (ASA), which means backdoor adversaries can carry out filtering attacks through data sharing. Therefore, this paper designs a blockchain‐based proxy re‐encryption (PRE) scheme with cryptographic reverse firewall (BIBPR‐CRF) for IoV. In our proposal, CRF can promise the internal safety of vehicle units. More specifically, it can prevent ASA attacks while ensuring chosen plaintext attack (CPA)‐security. Meanwhile, the PRE algorithm can provide the confidential sharing and secure operation of data. Moreover, we use a consortium blockchain service center (CBSC) to store the first ciphertext and re‐encrypt it with smart contracts on the blockchain, which can avoid single point of failure and achieve higher efficiency compared to proxy servers. Finally, we evaluate the performance of BIBPR‐CRF with regard to communication cost, computational cost, and energy consumption. Our proposal is the most fitting for IoV application, in contrast with the other three schemes.

Revocable and Fog-Enabled Proxy Re-Encryption Scheme for IoT Environments.

As technology advances rapidly, a diverse array of Internet of Things (IoT) devices finds widespread application across numerous fields. The intelligent nature of these devices not only gives people more convenience, but also introduces new challenges especially in security when transmitting data in fog-based cloud environments. In fog computing environments, data need to be transmitted across multiple devices, increasing the risk of data being intercepted or tampered with during transmission. To securely share cloud ciphertexts, an alleged proxy re-encryption approach is a commonly adopted solution. Without decrypting the original ciphertext, such a mechanism permits a ciphertext intended for user A to be easily converted into the one intended for user B. However, to revoke the decryption privilege of data users usually relies on the system authority to maintain a user revocation list which inevitably increases the storage space. In this research, the authors come up with a fog-based proxy re-encryption system with revocable identity. Without maintaining the traditional user revocation list, the proposed scheme introduces a time-updated key mechanism. The time-update key could be viewed as a partial private key and should be renewed with different time periods. A revoked user is unable to obtain the renewed time-update key and hence cannot share or decrypt cloud ciphertexts. We formally demonstrate that the introduced scheme satisfies the security of indistinguishability against adaptively chosen identity and chosen plaintext attacks (IND-PrID-CPA) assuming the hardness of the Decisional Bilinear Diffie-Hellman (DBDH) problem in the random oracle model. Furthermore, compared with similar systems, the proposed one also has lower computational complexity as a whole.

Security of End-to-End medical images encryption system using trained deep learning encryption and decryption network

The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.

On the Security of Selectively Encrypted HEVC Video Bitstreams

With the growing applications of video, ensuring its security has become of utmost importance. Selective encryption (SE) has gained significant attention in the field of video content protection due to its compatibility with video codecs, favorable visual distortion, and low time complexity. However, few studies consider SE security under cryptographic attacks. To fill this gap, we analyze the security concerns of encrypted bitstreams by SE schemes and propose two known plaintext attacks (KPAs). Then the corresponding defense is presented against the KPAs. To validate the effectiveness of the KPA, it is applied to attack two existing SE schemes with superior visual degradation in HEVC videos. Firstly, the video sequences are encoded using H.265/HEVC. During encoding, the selected syntax elements are recorded. Secondly, the recorded syntax elements are imported into the HEVC decoder. By utilizing the encryption parameters and the imported data in the decoder, it becomes possible to reconstruct a significant portion of the original syntax elements before encryption. Finally, the reconstructed syntax elements are compared with the encrypted syntax elements in the decoder, allowing the design of a pseudo-key stream (PKS) through the inverse of the encryption operations. The PKS is used to decrypt the existing SE scheme, and the experimental results provide evidence that the two existing SE schemes are vulnerable to the proposed KPAs. In the case of single bitstream estimation (SBE), the average correct rate of key stream estimation exceeds 93%. Moreover, with multi-bitstream complementation (MBC), the average estimation accuracy can be further improved to 99%.

Efficient provable dual receiver hybrid and light weight public key schemes based on the discrete logarithm problem without pairings

AbstractDual‐receiver proxy re‐encryption is a cryptographic technique that enables secure data sharing among multiple authorized users or entities. It has gained significant attention for its ability to manage access permissions, data confidentiality, and streamline communication channels. These schemes have been widely used in various applications, including healthcare systems, cloud computing, Internet of Things (IoT) systems, collaborative environments, and secure communication channels. This paper aims to propose two proxy re‐encryption schemes for dual receivers without pairings. The first is dual receiver lightweight proxy re‐encryption without pairings (DR‐LWPRE‐WP), which uses a public key scheme to reduce computational complexity. The second is dual receiver hybrid proxy re‐encryption without pairings (DR‐HPRE‐WP), which incorporates public key and symmetric key schemes. Both schemes offer protection against selected plaintext attacks based on the decisional Diffie‐Hellman principle. The DR‐LWPRE‐WP scheme reduces computation by approximately 53% compared to pairing‐based schemes, making it suitable for lightweight applications like the Internet of Things. The computational efficacy of these schemes offers significant benefits for resource‐constrained environments and practical implementations.

Optical phase image encryption using stokes parameters and singular value decomposition

Abstract In this paper, we propose an optical asymmetric phase image encryption method in which the vectorial light field is used to encode the data. In transverse plane, the vectorial light field has spatially varying polarization distributions where we are allowed to have a greater number of degrees of freedom. In this scheme, the input image is first phase encoded and then modulated by a phase encrypting key, synthesized from the speckles obtained by the scattering of Hermite–Gaussian beams. The modulated image is further processed using fractional Fourier transform with a specific order (α). A pixel scrambling operator is utilized to increase the randomness to further enhance the security and singular value decomposition approach is employed to add the nonlinearity in the encryption process. Now, the stokes parameters, i.e. S 1 and S 2 are calculated using the light intensities correspond to different polarizations. S 1 is used as the encrypted image for transmission and S 2 is reserved as one of the private decryption keys. The robustness of the proposed technique is tested against various existing attacks, such as known plaintext attack, chosen plaintext attack, and contamination attacks. Numerically simulated results validate the effectiveness and efficiency of the proposed method.

Integration of a novel 3D chaotic map with ELSS and novel cross-border pixel exchange strategy for secure image communication

This paper proposes a robust image encryption algorithm that utilizes a Novel three-dimensional (3D) Chaotic map and an Enhanced Logistic Sine System (ELSS). We leverage the unpredictability of 3D chaotic dynamics alongside the complexity of ELSS and DNA Sequence to forge a formidable image encryption scheme. Firstly, the image pixels are converted from decimal to hexadecimal notation and sorted in a 1D pixel array carrying a unique sequence of three channels of the RGB image. Secondly, the secret key is appended to XOR, the values with that 1-D pixels array. Thirdly, values are sorted by performing the binary right shift operation and encoded into DNA. Fourthly, a novel chaotic map is used to perform scrambling operations. Lastly, a novel enormous keyspace ELSS is used to perform efficient Border and Cross-Border (B &CB) pixel exchange, further enhancing the encryption quality of the proposed algorithm. Comprehensive security analysis proved that the proposed algorithm exhibits remarkable resilience against powerful known and chosen plaintext attacks and other prevalent cryptanalysis attacks, including differential attacks and exhaustive key search attacks. Henceforth, the proposed algorithm’s superior security and low computational cost make it an ideal choice for real-time secure image communication across various platforms, including satellite, multimedia, and military communications.

Encryption algorithm based on improved four-dimensional chaotic system and dynamic DNA encoding

An image encryption algorithm is proposed based on the combination of genetic (DNA) random coding and chaotic mapping. An image encryption algorithm based on an improved new four-dimensional chaotic system and DNA coding is proposed to address the problem that a single coding method is prone to selecting plaintext attacks. Based on a four-dimensional chaotic system existing in the literature, a new four-dimensional hyperchaotic system is obtained through improvement. The initial value of the system is generated based on SHA-256, zigzag transform. The input key and four pseudo-random chaotic sequences are generated iteratively. DNA chunking encoding, arithmetic operation, and decoding are implemented for the image disrupted based on the zigzag transform. The two-dimensional matrix constituted based on the Chaotic Sequence of Chebyshev to obtain the scrambled and diffused ciphertext image. Simulation experiments and security performance analysis show that the algorithm enhances the correlation between the key and the plaintext, the randomness of the encryption process, and effectively improves the anti-attack capability [H. Chen, J. Zhao et al., Appl. Res. Comput. 10, 0434 (2021)]. In this paper, 512 × 512 × 3 peppers color images are used for testing, and the correlation coefficients of adjacent pixels of the encrypted images are all close to 0, and the information entropy is all more significant than 7.997, which is relative to the theoretical value of 8. The experimental results show that the proposed algorithm improves the security of the ciphertext, increases the critical space, and, at the same time, resists various attack methods.