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Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset. [ABSTRACT FROM AUTHOR]

The theory of quadratic residues plays an important role in cryptography. In 2001, Cocks developed an identity-based encryption (IBE) scheme based on quadratic residues, resolving Shamir's 17-year-old open problem. However, a notable drawback of Cocks' scheme is the significant expansion of the ciphertext, and some of its limitations have been addressed in subsequent research. Recently, Cotan and Teşeleanu highlighted that previous studies on Cocks' scheme relied on a trial-and-error method based on Jacobi symbols to generate the necessary parameters for the encryption process. They enhanced the encryption speed of Cocks' scheme by eliminating this trial-and-error method. Based on security analysis, this study concludes that the security of Cotan-Teşeleanu's proposal cannot be directly derived from the security of the original Cocks' scheme. Furthermore, by adopting the Cotan-Teşeleanu method and introducing an additional variable as a public element, this study develops a similar enhancement scheme that not only accelerates the encryption speed but also provides security equivalent to the original Cocks' scheme. [ABSTRACT FROM AUTHOR]

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.

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes security and privacy considerations paramount in virtual assistant platforms. The gaps and weaknesses in existing systems can be identified by researching the context of current practice concerning their features, usability, limits in security procedures, and privacy restrictions. Therefore, we present a framework that combines blockchain-based security with federated learning (FL) to address the current shortcomings of virtual assistant technology. The examination focuses on two primary facets of cutting-edge virtual assistants. Firstly, it evaluates existing IoT-based virtual personal assistant systems designed for persons with disabilities, examining their features, usability, and limitations. The aim is to identify the specific needs and requirements of individuals with disabilities, considering their unique challenges and preferences in utilizing virtual assistant technologies. Second, considering the sensitivity of the information sent between users and virtual assistants, it explores the issues of security and privacy that arise while using such systems. The investigation covers authentication, data encryption, access control, and data privacy rules to provide a snapshot of the prevailing state protecting virtual assistants. Besides this, the framework strengthens the privacy and security of virtual assistants using blockchain technology. Through several empirical trials, it is found that the framework maintains better performance and usability, along with the provision of robust security mechanisms to safeguard user data and guarantee privacy. [ABSTRACT FROM AUTHOR]

High security solutions are highly important in wireless medical environments, since patient data is confidential, sensitive and must be transmitted over a secure connection. Accordingly, a hybrid encryption method is proposed to ensure data confidentiality (RSA-2048 for key exchange using ACL in SDN with the addition of AES-256-CTR and a hashed secret key for data encryption), and the encrypted data is stored in a private blockchain with the DBFT consensus algorithm to ensure the integrity of data before it being accessed by a doctor’s application which decrypts and displays the relevant information. The system was programmed using Python, in an NS3.37 simulator installed on Ubuntu with a MySQL database created using the Apache XAMPP. The product turned out to be a highly secure system for transmitting data from a medical sensor to the doctor’s application, offering a throughput of approximately 9 Gbps for both encryption and decryption tasks, while the processing time equaled 0.014 µs per a 128-bit block size for both encryption and decryption, with latency amounting to 0.14 s per 1 KB of data, and the blockchain agreement time equaling 4 ms per 1 KB. [ABSTRACT FROM AUTHOR]

In the contemporary era, technology plays a significant role in our daily lives. Consequently, there is a growing emphasis on securing information transmitted across networks. To meet this need, numerous cryptographic systems have been developed to enhance security. The distinguishing factor among these systems lies in their capability to uphold fundamental criteria for information security, including authentication confidentiality and integrity. This paper introduces a method involving the use of DNA and lightweight cryptography[1-2-3]. This method utilizes for the encryption and decryption of data using DNA sequence and the SIMON cryptographic system—a symmetric encryption algorithm specifically designed for simplicity and efficiency. [ABSTRACT FROM AUTHOR]

Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness of MKEE scales in tandem with the volume of information encapsulated within the ciphertext. MKEE substantially reduced the size of the ciphertext, achieving an 88% decrease when incorporating ten plaintext values. To further reduce the size of the ciphertext in our proposal, a Modular Multiplicative Inverse method (MMI) is introduced. MMI experiments were conducted, demonstrating that we achieved a commendable 50% reduction in the ciphertext size. To validate the practicality of the proposed method, a case study was conducted on a diabetes dataset. By integrating MKEE and MMI, this study showed a data storage reduction of 94%. [ABSTRACT FROM AUTHOR]

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.

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a revolutionary paradigm that has gained significant prominence in recent years. It represents the interconnection of everyday objects, devices and machines to the internet, allowing them to collect, exchange and analyze data. As the number of connected objects grows exponentially, energy consumption and data security become essential challenges to ensure a sustainable development of green IoT. Data compression theory can be used as an enticing key to downsize the large amount of circulated information, since wireless communication is the prime energy consuming component in IoT devices. In addition, data encryption algorithms are crucial to protect sensitive information and ensure the integrity and confidentiality of IoT systems. Therefore, combining compression and encryption can lead to improved overall IoT system performance in terms of both power cost and data privacy. In this paper, a lightweight and secure compression approach is introduced to efficiently manage the energy expenditure and provide end-to-end security for energy constrained devices. This proposed algorithm combines the performance of LTC compression and AES encryption to securely encode collected data in IoT devices. The energy model analysis has shown that the suggested method is effective in terms of processing energy, reduces the volume of transmitted data and guarantees end-to-end information privacy. [ABSTRACT FROM AUTHOR]

Addressing privacy concerns is critical in smart manufacturing where sensitive data is used for machine learning. Data protection is essential to ensure model accuracy while upholding data privacy. Homeomorphic encryption, an algorithm for privacy-preserving machine learning, achieves this by transforming data using a neural network with secret key weights. This process conceals private data while retaining the potential to learn classification models from the anonymized data. This paper introduces a comprehensive quality metric to assess homeomorphic encryption across conflicting criteria: security (regarding private data), machine learning adaptability (tolerance), and efficiency (regarding needed extra resources). Through experiments on various datasets, the metric proves its effectiveness in guiding optimal encryption parameter selection. Our findings highlight homeomorphic encryption's strong overall quality, positioning it as a valuable Industry 4.0 solution. By offering a simpler alternative to fully homomorphic encryption, it effectively addresses privacy concerns and enhances data usability in the context of smart manufacturing. [ABSTRACT FROM AUTHOR]

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.

Cryptography field, which enables secure communication between civilians, governmental organizations, military forces, and many more, addresses security, confidentiality, and integrity of information being conveyed regardless of the medium used. Protection of priceless information resources on intranets, the Internet, and the cloud has become a vital demand of contemporary electronic security systems. In this paper, we deploy homomorphic data encryption (HDE) technique on Bipolar-valued Fuzzy Sub-Bigroups (BVFSB). Intuitionistic Fuzzy Sets were presented by Atanassov in 1986, as a modification of Fuzzy Sets, by taking into consideration the grade of membership value and non-membership value for each element in the universe. A fuzzy algebraic structure known as Bipolar-valued Fuzzy sub-bigroup of a bigroup has been established by applying the concept of bipolar-valued Fuzzy sets to bigroups. Some of the theorems related to homomorphism and anti-homomorphism, are stated and proved. [ABSTRACT FROM AUTHOR]

Existing Fully Homomorphic Encryption schemes mainly operate on integer data in the finite field. However, in many real-life applications, the data is in floatingpoint number format, and the homomorphic encryption of floating-point information is challenging. This paper presents a new homomorphic encryption method for matrices whose elements are floating-point numbers. The proposed homomorphic encryption of Floating-Point Data Matrices is applied for secure image processing where the loss of accuracy due to the finite length of the floating-point format representation is insignificant. Here, the encryption/decryption keys are also in floating-point format. New techniques of homomorphic weighted addition, multiplication, and DCT calculation are presented. Homomorphic proxy re-encryption is the novelty of the proposed method. [ABSTRACT FROM AUTHOR]

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network. This paper presents an image protection scheme for healthcare applications to protect patients' medical image data exchanged in IoMT networks. The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance. The optimized S-box has an excellent nonlinearity score of 112. The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022, entropy values higher than 7.999, and NPCR values around 99.6%. To reveal the efficacy of the scheme, several comparison studies are presented. These comparison studies reveal that the novel protection scheme is robust, efficient, and capable of securing healthcare imagery in IoMT systems. [ABSTRACT FROM AUTHOR]

With the growing adoption of cloud-based technologies, maintaining the privacy and security of cloud data has become a pressing issue. Privacy-preserving encryption schemes are a promising approach for achieving cloud data security, but they require careful design and implementation to be effective. The integrated approach to cloud data security that we suggest in this work uses CogniGate: the orchestrated permissions protocol, index trees, blockchain key management, and unique Opacus encryption. Opacus encryption is a novel homomorphic encryption scheme that enables computation on encrypted data, making it a powerful tool for cloud data security. CogniGate Protocol enables more flexibility and control over access to cloud data by allowing for fine-grained limitations on access depending on user parameters. Index trees provide an efficient data structure for storing and retrieving encrypted data, while blockchain key management ensures the secure and decentralized storage of encryption keys. Performance evaluation focuses on key aspects, including computation cost for the data owner, computation cost for data sharers, the average time cost of index construction, query consumption for data providers, and time cost in key generation. The results highlight that the integrated approach safeguards cloud data while preserving privacy, maintaining usability, and demonstrating high performance. In addition, we explore the role of differential privacy in our integrated approach, showing how it can be used to further enhance privacy protection without compromising performance. We also discuss the key management challenges associated with our approach and propose a novel blockchain-based key management system that leverages smart contracts and consensus mechanisms to ensure the secure and decentralized storage of encryption keys. [ABSTRACT FROM AUTHOR]

Multi-layer satellite network (MLSN) is an important part of the Space-Air-Ground Integrated Network (SAGIN), it has obvious advantages in coverage density, business processing, service efficiency and other comprehensive capabilities compared with ordinary single-layer satellite networks. However, due to the fact that many space nodes, highly dynamic topology changes and wireless links scattered in free space, MLSNs are facing the dual challenges of inefficient organization and lack of security. In this case, this paper considers providing secure encryption decision support for the return of data obtained by low earth orbit (LEO) observation satellites relying on the local autonomous region of multi-layer satellite space. By quantifying the security strength of encryption algorithms, the work to be optimized is described as the problem of maximizing the security strength under the constraint of return delay of the data. This paper thus proposes a solution based on deep reinforcement learning (DRL), which relies on reasonably setting the key supporting elements such as state, action and reward closely related to the problem to achieve fast and reliable decision output, and the simulation results show that the proposed method has better performance than many comparison methods. [ABSTRACT FROM AUTHOR]

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.

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospitalmanagement systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attributebased encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively. [ABSTRACT FROM AUTHOR]

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability. Chaotic maps are associated with the other domains to construct robust encryption algorithms. There exist numerous encryption schemes in the literature based on chaotic maps. This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem. The core notion of the original algorithm was based on permutation and diffusion. A bitlevel permutation approach was used to do the permutation row-and column-wise. The diffusion was executed in the forward and backward directions. The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem. This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack. The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images. The results in this manuscript suggest that, in addition to standard statistical evaluations, thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application. Moreover, the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data. The results of the performance analysis indicate the exact recovery of the original data. To make the cryptosystem useful for applications requiring secure data exchange, a few further improvement recommendations are also suggested. [ABSTRACT FROM AUTHOR]

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.

Although it was first developed and studied in the late 1970s and early 1977s, the Data Encryption Standard (DES) algorithm has grown in popularity. There are two causes for this occurrence. First, the DES algorithm's complex mathematical structure allows it to serve as the theoretical foundation for a wide variety of applications. Second, the encryption technique works quite well in practice for a variety of applications when implemented correctly. In this paper, we undertake a thorough and practical review of the theoretical aspects of this sort of encryption algorithm and demonstrate how they have been implemented by executing multiple encryption configurations. [ABSTRACT FROM AUTHOR]

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95 % for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87 % and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization. [ABSTRACT FROM AUTHOR]

With the continuous improvement of global informatization, computer networks have basically reached complete popularization. In today's society, it has become a major application and provides corresponding services for various industries. Therefore, it is necessary to effectively protect various information in the network. Therefore, this article provided a detailed discussion on the problems existing in computer network information security. These issues included their own reasons, hacker intrusion, and spam. Based on this, this article explored computer network information security protection strategies such as deploying data encryption, timely patching system vulnerabilities, and installing firewalls and antivirus software. The experimental results showed that the network output values of the clustering algorithm were: 0.888 for level 1, 0.725 for level 2, 0.678 for level 3, 0.461 for level 4, and 0.211 for level 5. These were all within the scope. [ABSTRACT FROM AUTHOR]

To address the issues of low confidentiality and poor real-time performance in the encryption process of accounting system data, a proposed solution based on artificial intelligence is presented. Firstly, by studying the knowledge and reasoning abilities utilized by artificial intelligence technology to simulate human experts in solving complex problems, the fundamental structure of the accounting system is obtained. Then, the data prior to encryption is processed using the wavelet denoising method to eliminate noise points from the data. Additionally, a chaotic parameter modulation encryption method is introduced, utilizing a logistic chaotic mapping model to modulate the data parameters. Simultaneously, during encryption and decryption analysis, iterative criteria for data parameters are established to reduce the number of iterations and achieve real-time data encryption. Finally, experimental results demonstrate that this method possesses higher efficiency in terms of real-time performance, integrity, and confidentiality, providing superior real-time data encryption capabilities. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) enables devices to get connected to the internet. Once they are connected, they behave as smart devices thereby releasing sensitive data periodically. There is a necessity to preserve the confidentiality and integrity of this data during transmission in public communication channels and also permitting only legitimate users to access their data A key challenge of smart networks is to establish a secure end-to-end data communication architecture by addressing the security vulnerabilities of data users and smart devices. The objective of this research work is to create a framework encompassing Ciphertext policy Attribute-based Encryption scheme using block encryption and BLAKE hashing technique. An improved Pairing-Free-Ciphertext policy Attribute-based encryption algorithm has been developed to overcome the aforementioned challenges. Further, a comparative study has been performed between the proposed scheme and the different encryption algorithms. It is found that the proposed scheme scores well over the already existing schemes. The scheme is evaluated in terms of execution time and communication overhead. The robustness of the proposed scheme is also analyzed from the perspective of several security goals. [ABSTRACT FROM AUTHOR]

Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms. By incorporating new, cutting-edge technologies to a larger extent without using more infrastructures, the information technology platform is anticipating a completely new level of development. The following concepts are proposed in this research paper: 1) A reliable authentication method Data replication that is optimised; graph-based data encryption and packing colouring in Redundant Array of Independent Disks (RAID) storage. At the data centre, data is encrypted using crypto keys called Key Streams. These keys are produced using the packing colouring method in the web graph's jump graph. In order to achieve space efficiency, the replication is carried out on optimised many servers employing packing colours. It would be thought that more connections would provide better authentication. This study provides an innovative architecture with robust security, enhanced authentication, and low cost. [ABSTRACT FROM AUTHOR]

The applications of wireless sensor network (WSN) exhibits a significant rise in recent days since it is enveloped with various advantageous benefits. In the medical field, the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians. WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human. The data of different persons, time, places and networks have been linked with certain devices, which are collectively known as Internet of Things (IOT); it is regarded as the essential requirement of people in recent days. In the health care monitoring system, IOT plays a magnificent role, which has produced the real time monitoring of patient's condition. However the medical data transmission is accomplished quickly with high security by the routing and key management. When the data from the digital record system (cloud) is accessed by the patients or doctors, the medical data is transferred quickly through WSN by performing routing. The Probabilistic Neural Network (PNN) is utilized, which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing (DSR) protocol and Energy aware and Stable Routing (ESR) protocol. While performing routing, the secured transmission is achieved by key management, for which the Diffie Hellman key exchange is utilized, which performs encryption and decryption to secure the medical data. This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio. [ABSTRACT FROM AUTHOR]

The arrival of the Internet of Things and 5G technology enables users to rely on edge computing platforms to process massive data. Data sharing based on edge computing refines the efficiency of data collection and analysis, saves the communication cost of data transmission back and forth, but also causes the privacy leakage of a lot of user data. Based on attribute-based encryption and blockchain technology, we design a fine-grained access control scheme for data in edge computing, which has the characteristics of verifiability, support for outsourcing decryption and user attribute revocation. User attributes are authorized by multi-attribute authorization, and the calculation of outsourcing decryption in attribute encryption is completed by edge server, which reduces the computing cost of end users. Meanwhile, We implemented the user's attribute revocation process through the dual encryption process of attribute authority and blockchain. Compared with other schemes, our scheme can manage users' attributes more flexibly. Blockchain technology also ensures the verifiability in the process of outsourcing decryption, which reduces the space occupied by ciphertext compared with other schemes. Meanwhile, the user attribute revocation scheme realizes the dynamic management of user attribute and protects the privacy of user attribute. [ABSTRACT FROM AUTHOR]

With the continuous destruction of the data transmitted in the protection network system, data encryption is an important research object in the network security technology, and it is the core to ensure the correctness of the data transmitted in the network. Through further research and Analysis on the basic technologies of cryptography and data transmission, combined with several common data encryption algorithms and data communication methods, the Windows sock communication method and mixed encryption module of mixed encryption data transmission system are designed and studied. In this module, a pair of RSA keys are generated by the software system first, which are imported into the hybrid encryption process to encrypt the des keys randomly generated in the system, which optimizes the system design objectives and the main functions of the system. In this paper, through further careful and in-depth research on the two classical encryption algorithms DES (Data Encryption Standard) and RSA (Rivest-Shamir-Adleman), we propose and de-sign a method of heterogeneous network data security transmission based on hybrid encryption. The experiment is programmed by visual c++, and the mixed encryption data transmission system is simulated and tested. The experimental results show that the design and implementation of the hybrid encryption data transmission system overcomes the shortcomings of the previous systems, such as slow speed and difficult key transmission. [ABSTRACT FROM AUTHOR]

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.

Wireless Sensor Networks (WSNs) are widely utilized in a variety of applications. Attackers might interfere with equipment, and eavesdrop on conversations. Attackers can also modify the data which has been communicated or connect unauthorized devices to the network if such devices are deployed in the physical environment. To ensure WSN security, messages exchanged between communicating nodes must be encrypted, and for encryption and decryption, the network must store a key. Consequently, Key management is of significant importance in achieving security in WSN. Obtaining such a key arrangement in a resource-constrained environment is required for secure and energy-efficient data transmission in WSN. Current algorithms have certain security issues, such as vulnerability to plaintext attacks, brute force attacks, side-channel attacks, and computational complexity. Cryptography along with clustering has been found to be a practical and efficient technique to improve the secure and energy-efficient data transmission in WSNs. In this article we have used an asymmetric Elliptic Curve Cryptography (ECC) method for key generation whereas encryption and decryption of data, has been done by a hybrid of Advanced Encryption Standard (AES) and ECC cryptography A novel algorithm has been developed that incorporates both techniques (Advanced Encryption Standard (AES) and ECC cryptography) along with clustering through standard LEACH protocol for improving energy efficiency, data security, and network lifetime. The proposed algorithm has been able to overcome the issue of key exchange that plagues AES, simpler than ECC and more reliable than AES. More significantly, it is able to encounter a variety of security threats including side-channel attacks. The proposed algorithm is better and has been compared to current techniques with respect to time complexity, encryption time and decryption time to demonstrate its efficacy. [ABSTRACT FROM AUTHOR]

In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient's critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored data are categorized as significant and non-significant by Artificial Neural Networks (ANN). The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer. Upon receiving the request from a secondary user, the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user. Once the key matches the key in the hidden layer, the preserved information will be shared between the users. Due to the enhanced privacy preserving key generation, the proposed work prevents the tracking of keys by malicious users. The outcomes reveal that the introduced work provides improved success rate with reduced computational time. [ABSTRACT FROM AUTHOR]

Protecting and concealing sensitive data in the modern world is challenging. Due to insufficient protection and privacy, it is feasible for critical information to be fabricated. This led to a significant financial loss for someone. The intended recipient must be trusted with sensitive information and be able to independently authenticate the accuracy of the information by independently checking the specifics. There are several driving the rise in QR codes used for information transmission. Due to their enormous capacity for storing information, QR codes are vital for dissemination. However, most currently deployed QR code solutions employ insecure data formats and never employ encryption. Secure QR Code is data protection and data concealing available technology. The Quick Response (QR) code is widely used and accessible without extensive technical training. Now, the user data stored in a QR code is effectively public knowledge and occasionally even illegally used. To address the abovementioned issues, this study's authors propose a novel QR code encryption system. Using the image's mathematical processing method, we may apply the equivalence class principle to the ordered equations of the two-dimensional code, producing the desired cryptographic result. This method exploits the unique visual properties of the QR code. Only a QR code reader can decode the code's useful information, which is too complex for standard reading methods. It will be utilised to address issues in speedy business client data protection Security, commodity anti-counterfeiting, and bicycle sharing QR codes. [ABSTRACT FROM AUTHOR]

The purpose of this study was to provide a comprehensive understanding of the usage of software encryption and its importance in securing data and communications. Although previous studies about data and communication encryption have focused on cybersecurity, very few have given attention to examining the relationship between the use of cryptographic software with data and communication encryption. Software encryption tools can enhance data privacy and assist in protecting sensitive data. To fill this research gap, this study used a quantitative study on the usage of a cryptographic software tool for data communication encryption. Data was collected through a survey deployed on LinkedIn between April 1, 2023, and April 29, 2023. The target population for the survey was college instructors and working professionals in the field of IT. A total of 61 participants completed the survey. The findings indicated that a stronger cryptographic tool and data encryption would increase users' perception of data security when sending and receiving sensitive data. This study suggested that stronger cryptography and data encryption would increase users' confidence when sending and receiving sensitive data. [ABSTRACT FROM AUTHOR]