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Summary: Image encryption is crucial for protecting image privacy and ensuring security. Encrypting large batches of images of different types and sizes simultaneously with losslessly decryption is often necessary. This paper proposes an optical asymmetric multi-image encryption algorithm to meet these demands. First, plaintext images are converted into one-dimensional pixels and blocked. Image information, image count, and pixels are stored in corresponding areas and reassembled. Unit equal-modulus vector decomposition (UEMD) and phase truncation generate the ciphertext image and keys. The decrypted image is reconstructed from the ciphertext’s information and quantity areas. Asymmetric encryption with different keys for encryption and decryption enhances security, while UEMD ensures lossless recovery and robustness. Experiments demonstrate the proposed algorithm’s efficiency in encrypting multiple grayscale and color images of varying sizes, providing high security, and lossless recovery. This technology offers superior protection for sensitive image data, enhancing encryption system practicality and digital security.

In the era of information technology, the exponential growth of data volume has compelled users to entrust their data to database service providers for efficient management. While traditional methods such as user authentication, security auditing, and access control have been implemented to safeguard data security across databases, cloud servers may face security challenges due to excessive internal administrator permissions. The audio technology of the Internet of Things (IoT) offers numerous advantages, including high bandwidth, short data transmission time, long transmission distance, and cost-effectiveness. It has found widespread applications in military, technological, industrial, and everyday life settings. With the advancements in modern information technology and the development and enhancement of wireless radio frequency technology in the IoT sector, IoT-based wireless audio interaction systems utilizing Wi-Fi have gradually become integrated into people's lives. These systems have enriched people's daily experiences and are increasingly popular among consumers. This paper, based on the theory of functional applied linguistics, combs the basic theoretical framework and the practical application prospect of functional Applied Linguistics of systemic language, especially emphasizes the important influence and application enlightenment of systemic functional linguistics on language teaching, and combines the ''Recent Development Zone'' The basic concepts of contemporary language and literature education, such as scaffolding, are summarized and analyzed. The paper establishes a comprehensive theoretical research framework for college spoken English teaching, and through the comprehensive theoretical analysis of communicative teaching methods and practical exploration of some problems that students may appear, it puts forward more effective and targeted specific solutions.

Along with the continuous growth and improvement of computer and communication technologies, wireless sensors are needed in more and more places to fulfil specific tasks. A lightweight data encryption mechanism is designed for the limited processing power, storage space and energy of wireless sensor networks. The mechanism improves the RC6 algorithm and adds the “symmetric layer” operation. In order to further improve the security of the ciphertext and the strength of the data encryption, the AES-128 encryption algorithm is used in the data link layer, the network layer, and the application layer, respectively. The experiment is based on the STM32L series of ultra-low-power ARM processors, and the operating frequency of the STM32L controller is tuned to 64 MHz to obtain faster efficiency; three groups of experiments on the encryption mechanism are carried out by using the original RC6 algorithm and the improved algorithm, and the lengths of the keys used in the experiments are 16 bytes, 24 bytes, and 32 bytes, respectively, and the data-receiving and transmitting functions are tested separately. This avoids the delay of transceiver mode switching when using a single module for communication, and improves the quality and stability of multi-machine bidirectional communication.

Cipher-block chaining (CBC) is a widely used encryption mode for smart grid data, however, the chain structure leads to low encryption and decryption efficiency. To overcome this issue, this paper proposes a novel tree-CBC (TCBC) block encryption method. First, the smart grid plaintext is preprocessed according to the security level of the data. Second, the chain structure of the CBC mode is converted into a tree structure, and for the data block with a higher security level, its location is deeper in the encryption tree. Third, since the overall security and encryption time of data encrypted by TCBC depend on the number of forks of the encryption tree, this paper proposes a method to determine the optimal number of forks of TCBC based on the weights of the encryption time and security of the data blocks. The experiment shows that the encryption efficiency of the TCBC mode is improved by nearly 50% compared to the original CBC mode. And as the data size increases, the efficiency improvement becomes more obvious. Furthermore, compared with widely used encryption modes such as CFB, OFB, CTR, and PCBC, the proposed TCBC mode has higher encryption and decryption efficiency.

A 3D-logistic-fraction-sine (3D-LFS) chaotic map and NFSR are used to encrypt sensitive data in a new, secure, and lightweight manner. The algorithm can process data in a variety of formats, including images, random test data streams, and legal PDF documents accessible to the public. Randomness of the encrypted image has been verified using the NIST SP800-22 statistical test suite. Several well-known attacks, such as differential, noise, and crop attacks, have been utilised to validate the algorithm’s robustness. Finally, the proposed technique has been compared with other existing algorithms and the data (presented in tables) confirm that the proposed algorithm is competitive and can be applied in embedded systems (and IoT devices) due to its performance.

Summary: Genomic researchers increasingly utilize commercial cloud service providers (CSPs) to manage data and analytics needs. CSPs allow researchers to grow Information Technology (IT) infrastructure on demand to overcome bottlenecks when combining large datasets. However, without adequate security controls, the risk of unauthorized access may be higher for data stored on the cloud. Additionally, regulators are mandating data access patterns and specific security protocols for the storage and use of genomic data. While CSP provides tools for security and regulatory compliance, building the necessary controls required for cloud solutions is not trivial. Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud-native genomics data repository and analytics platform that implements a “five-safes” framework to provide security and governance controls to data contributors and users, leveraging CSP for sharing and analysis of genomic datasets without the risk of security breaches or running afoul of regulations.

Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.

Despite the great efforts to secure wireless sensor network (WSN), the dynamic nature and the limited resources of sensor nodes make searching for a secure and optimal network structure an open challenge. In this paper, we propose a novel encryption schema based on Elliptic Curve Cryptography (ECC) and homomorphic encryption to secure data transmission in WSN. The proposed encryption schema is built upon GASONeC algorithm (Elhoseny et al., 2014) that uses genetic algorithm to build the optimum network structure in the form of clusters. ECC is used to exchange public and private keys due to its ability to provide high security with small key size. The proposed encryption key is 176-bit and is produced by combining the ECC key, node identification number, and distance to its cluster head (CH). To reduce energy consumption of CH, homomorphic encryption is used to allow CH to aggregate the encrypted data without having to decrypt them. We demonstrated that the proposed method is capable to work with different sensing environments that need to capture text data as well as images. Compared with the state-of-the-art methods, our experimental results demonstrated that our proposed method greatly improve the network performance in terms of lifetime, communication overhead, memory requirements, and energy consumption.