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In order to meet the data storage and access needs of coal mine personnel precise positioning system based on new positioning technologies such as UWB, the data structure, sampling frequency, data volume and storage space occupied by the personnel precise positioning system are analyzed, and the main features for system data generation and use are summarized, a hybrid data storage architecture based on Redis, MySQL and files to respectively store hot data, warm data and cold data is proposed, and the key design of Redis, MySQL, file storage, data encryption and data query method based on Web API data interaction service are described in detail. By using simulated data testing, it was verified that the storage solution has high data access efficiency, can meet the needs of data storage and business function data query.

Abstract Cryptography is crucial in protecting sensitive information and ensuring secure transactions in a time when data security and privacy are major concerns. Traditional cryptography techniques, which depend on mathematical algorithms and secret keys, have historically protected against data breaches and illegal access. With the advent of quantum computers, traditional cryptography techniques are at risk. In this work, we present a cryptography idea using logical phi-bits, which are classical analogues of quantum bits (qubits) and are supported by driven acoustic metamaterials. The state of phi-bits displays superpositions similar to quantum bits, with complex amplitudes and phases. We present a representation of the state vector of single and multi-phi-bit systems. The state vector of multiple phi-bits system lies in a complex exponentially scaling Hilbert space and is used to encode information or messages. By changing the driving conditions of the metamaterial, the information can be encrypted with exceptional security and efficiency. We illustrate experimentally the practicality and effectiveness of encoding and encryption of a message using a 5 phi-bits system and emphasize the scalability of this approach to an N phi-bits system with the same processing time.

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 order to address the problems of the single encryption algorithm, such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment, we propose a Hadoop based big data secure storage scheme. Firstly, in order to disperse the NameNode service from a single server to multiple servers, we combine HDFS federation and HDFS high-availability mechanisms, and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage. Then, we improve the ECC encryption algorithm for the encryption of ordinary data, and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated. To accelerate the encryption, we adopt the dual-thread encryption mode. Finally, the HDFS control module is designed to combine the encryption algorithm with the storage model. Experimental results show that the proposed solution solves the problem of a single point of failure of metadata, performs well in terms of metadata reliability, and can realize the fault tolerance of the server. The improved encryption algorithm integrates the dual-channel storage mode, and the encryption storage efficiency improves by 27.6% on average.

The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices’ computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE’s high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses.

The edge devices will produce enormous quantities of data daily as the Industrial Internet of Things (IIoT) expands in scope. Still, most IIoT data is stored in data centers, making it challenging to transfer data between domains safely. Smart logistic products have dramatically changed due to the prevalence of decentralized edge computing and blockchain in the industry sector. To address the need to exchange data between logistics networks, we proposed a novel decentralized hierarchical attribute-based encryption (HABE) scheme combining edge computing and blockchain. To begin, we offer an IoT data encryption strategy in which edge devices can send data to a nearby cloud network for data processing while maintaining privacy. In addition, we developed a blockchain-integrated data-sharing scheme that makes it possible for users to share data via the use of edge and cloud storage. In particular, an IoT device incorporates an encryption-based authentication system to verify users’ access rights at the network’s periphery in a decentralized manner. Using HABE, we provide a blockchain-integrated architecture for the IoT that protects user privacy. The suggested design utilizes the edge and cloud network paradigms and HABE to maintain privacy and works well with smart logistics applications. The authentication time of the proposed model is reduced by 1.5 times compared with the centralized model. The analyses and experimental findings show that the proposed blockchain-integrated edge computing architecture is better than the existing schemes in terms of data sharing, data privacy, and 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.

With the increasing use of optical fiber communication technology in the Industrial Internet of Things, cryptographic algorithms play a crucial role in ensuring the security of data transmission in embedded device environments.The SM4 packet cipher algorithm, developed independently in our country, is widely applied to wireless LAN and Internet of Things data encryption.However, the software-based encryption and decryption processes are relatively slow, which hampers their application in scenarios requiring high real-time performance, especially for embedded devices.To address this issue, a high-performance and secure optical fiber communication system was designed based on the FPGA platform and the SM4 algorithm.The FPGA was used to implement the MAC layer interface for SM4 algorithm encryption and decryption, as well as data transmission.Besides, an optimization scheme for the hardware implementation architecture of the SM4 algorithm was proposed.The critical path was shortened by employing a pipeline method, thereby improving the system clock frequency.Additionally, parallel processing of S-box transformation was accelerated to enable efficient data replacement.To reduce data reading delays, a dual-cache processing method was combined to facilitate easier processing of cache data and significantly reduce packet loss rates.This scheme greatly enhanced system data throughput.Experimental results demonstrate that compared to similar designs, the throughput of the SM4 algorithm encryption and decryption module in this scheme reaches up to 25.6 Gbit/s, with minimal differences in resource consumption.Due to limitations imposed by the 10-gigabit SFP+ optical module, the throughput of the entire optical fiber communication system reaches 9.4 Gbit/s.For 128-bit data, the average encryption speed is 0.47 μs/bit and the average decryption speed is 0.28 μs/bit, which can be applied to a variety of secure communication scenarios in the internet of things.

In this study, we present a new cryptosystem named Deoxyribose Nucleic Acid (DNA) secret writing with the Laplace transform of the Mittag-Leffler function. The method is proper for encrypting large files. In this technique, we consider the original message as binary sequence. These binary streams corresponding to the plain text is transformed to DNA bases by utilizing DNA encoding, then the DNA codes are transformed to positive integers. We apply the Laplace transform to these numbers which are coefficients of the expansion of the Mittag-Leffler function. To provide multi-stage protection, the outcome coefficients are transformed to binary sequences and other level of encryption with cumulative XOR is applied and equivalent MSBs obtained at every iteration are utilized for building cipher text. Decryption is implemented in the opposite way. We employ monobit test, correlation analysis for measuring the reliability of encryption, and Python programming language to obtain secret message, the plain text, and computations of statistical tests.

In this article, we study some new properties of twin primes and algorithms for their generation. We find the necessary conditions to generate a pair of twins. These conditions seem to indicate that the conjecture is true, namely, there are infinitely many twin primes. Furthermore, we developed some algorithms that are very useful from a computer science point of view, which can be applied in cryptography and data encryption.

Healthcare institutions generate massive amounts of valuable patient data in the digital age. Finding the right balance between patient privacy and the demand for data-driven medical enhancements is essential. Since data privacy has become increasingly important, robust technologies must be developed to safeguard private data and allow meaningful exploration. This issue was addressed by ShinyAnonymizer, which was first created to anonymize health data. It achieves this by rendering anonymization methods easily available to users. The enhanced version of ShinyAnonymizer, with an essential improvement in performance, is presented in this study. We explain the merging of data analysis, visualization, and privacy-focused statistics paradigms with data anonymization, hashing, and encryption, offering researchers and data analysts an extensive collection of tools for trustworthy data management.

With the adoption of a novel system architecture characterized by “physical distribution and logical unity” in new-type power systems， the support system for power grid scheduling is reconstructed. Under the new architecture， the human-machine cloud terminal （hereinafter referred to as “cloud terminal”） can realize local and remote undifferentiated browsing. However， the cloud terminal is also faced with new security challenges. First， the new architecture and the human-machine access process are analyzed， and the security problems are pointed out. Then， a unified security authentication method is proposed based on hardware fingerprint to authenticate human-machine cloud terminal is proposed. By use of digital certificates for power scheduling and biometric identification technology， multi-factor user identity authentication is performed. Startup authentication and service invocation verification are carried out， and encrypted transmission of service communication data is made possible to ensure service startup and access security. The service communication data is encrypted and transmitted to ensure the security of service startup and access. Finally， the performance of service authentication and encryption is tested， and the authentication and encryption strategies for different service request sizes are proposed. The test results show that the whole link security of human-machine access is guaranteed through the authentication of the cloud terminal， user identity， service， and communication data encryption.

Block cipher algorithms are a very important issue in the field of information security. Their simple structure and software-based encryption allow users to implement them in several applications such as: data security and cloud computing. In this paper, we reviewed 8 block ciphers that had been presented in the recently five years and were used in different applications. The 8 block ciphers are: DES, 3DES, Blowfish, Twofish, PRISENT, KLEIN, IDEA and AES. All of them are symmetric block ciphers with different designs. The comparative results showed that the block ciphers can still be used in different applications and fields. They showed that many modifications had been presented in which, chaotic maps had been implemented in key generation to enhance the robustness of the block ciphers and its randomness. The comparative results also showed that the AES is one of the block ciphers that is still unbroken algorithm and still modified to suit other new applications. Since less robust ciphers have continuous modification and enhancement in order to be more robust against some of probable attacks, the length of the key and its complexity and the structure of the ciphers are essential directions for improving block ciphers.

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.

Abstract Cloud-edge data security is a key issue in the internet of vehicles (IoV), as the potential for data breaches increases as more vehicles are connected. As vehicles become smarter and more connected, the risk of unauthorized access to the data generated by the vehicles also increases. Data encryption is a highly effective security measure that is widely used to protect the IoV from malicious actors. By encrypting data, it becomes virtually impossible for unauthorized individuals to access the information. This ensures that only the intended parties can access the data, allowing for secure communication between cloud and edge. Data encryption is a cost-effective and reliable security measure that is essential for any organization that relies on the IoV. The IoV is characterized by the large volume of data that is exchanged between devices in cloud and edge. This necessitates the use of a strong encryption method, such as stream ciphering, which is particularly well-suited to this type of environment. Stream ciphering provides the highest levels of security, making it the ideal choice for securing data transmission in the IoV. Many stream ciphering algorithms use bitwise exclusive or (XOR) to encrypt the data stream, so the core is the generation of a pseudo-random key stream. This paper proves that the probability of the number 1 appearing in the middle part of the Zeckendorf representation is constant, which can be used to generate pseudo-random key stream sequences. The pseudo-random sequence generated by the linear feedback shift register (LSFR) is periodic, and the key sequence will be duplicated. The logistic chaos (LC) sequence is too sensitive to the disturbance of initial value, and its stability is poor. In this paper, our proposed ZPKG (key generator based on Zeckendorf presentation) algorithm solves these two main problems in stream ciphering. The generated key sequence not only has strong randomness, but also is infinitely long, and it is robust to the minor disturbance of the initial value. Graphical Abstract

Continuing education platforms are faced with problems such as the aging of online learning platforms and backward technology. The data center network is an essential infrastructure supporting the big data and cloud computing platform continuing education network. It is widely used for data-intensive and massively parallel computing tasks. The paper proposes a continuing education network data center management model based on this research background. At the same time, this paper proposes a real-time encryption method for distributed data in a multi-layer differential continuing education network based on fractional differential mathematical equations. The experimental study found that the fractional differential mathematical equations method can effectively control the stability of data transmission of continuing education receipt networks. This algorithm improves the efficiency of network data operation and controls the cost of encryption. The research results of this paper provide technical support for network education.

Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power of machine learning (ML) and deep learning (DL) algorithms to improve their self-driving learning experiences. The sudden surge in the number of AVs raises the need for distributed learning ecosystem to optimize their self-driving experiences at a rapid pace. Toward this goal, federated learning (FL) benefits, which can create a distributed learning environment for AVs. But, the traditional FL transfers the raw input data directly to a server, which leads to privacy concerns among the end-users. The concept of blockchain helps us to protect privacy, but it requires additional computational infrastructure. The extra infrastructure increases the operational cost for the company handling and maintaining the AVs. Motivated by this, in this paper, the authors introduced the concept of gradient encryption in FL, which preserves the users’ privacy without the additional computation requirements. The computational power present in the edge devices helps to fine-tune the local model and encrypt the input data to preserve privacy without any drop in performance. For performance evaluation, the authors have built a German traffic sign recognition system using a convolutional neural network (CNN) algorithm-based classification system and GeFL. The simulation process is carried out over a wide range of input parameters to analyze the performance at scale. Simulation results of GeFL outperform the conventional FL-based algorithms in terms of accuracy, i.e., 2% higher. Also, the amount of data transferred among the devices in the network is nearly three times less in GeFL compared to the traditional FL.

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.

This research purpose is to develop a reduction-based method for analysis of cryptographic properties of block cipher modes of operation in context of full disk encryption (FDE) via provable security technique, to study weaknesses of widely spread among existing FDE solutions mode XTS through building an adversary, that breaks security of XTS, and then to create a variation of the mode, that fixes detected weaknesses. This new mode of operation is called XEH (Xor-Encrypt-Hash) and it uses “light-weight” polynomial permutation for mixing blocks of a sector after their “XTS-like” encryption. The proposed block cipher mode of operation does not require any space for additional data, and it allows us to use it for system disk encryption, unlike DEC mode proposed in the end of 2021 by TC 26. XEH was proved to be secure in a provable security model, which is described in this paper. This mode is compared with the existing ones, which can be used to encrypt block-oriented devices: it was shown, that XEH provides higher security bound, than other compared modes, and involves almost no degradation in performance with respect to XTS.

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.

To clarify the copyrights of 3D models of oblique photography (3DMOP) and guarantee their security, a novel security protection scheme of 3DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3DMOP and thus further expand their application scope.