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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.

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 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 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.

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

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

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

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.

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.

Recent advances in mobile connection technology have been involved in every aspect of modern life. Even vehicles are becoming more connected, with the ability to communicate without human intervention. The main idea of connected vehicles is to exchange information to avoid a potential collision or to warn the driver about stop signs/lights. To achieve a wide range of connections between vehicles, they must be equipped with connected devices such as Bluetooth, wi-fi, and cellular connectivity. However, communication raises security issues with regard to cybersecurity attacks that attempt to collect confidential information or to take control of the vehicle by forcing unintended braking or steering. In this paper, we proposed a secure vehicle-to-vehicle (V2V) communication approach by deploying a secure communication protocol based on a key management process and a cryptography system to encrypt exchanged data. The proposed key management process was designed to resist many attacks and eliminate connections to the infrastructure for key generation. Since vehicles are equipped with embedded devices with limited computation resources, a lightweight cryptography algorithm was used. The light encryption device (LED) block cipher was used to encrypt exchanged data. The LED has a low implementation area on hardware and low power consumption. It is considered to be a perfect solution for security issues in connected vehicles. The proposed data encryption algorithm was synthesized with VHDL on the Xilinx Zynq-7020 FPGA using the Vivado HLS tool. The encryption algorithm was implemented only on the logic of the device. The achieved results proved that the proposed algorithm is suitable for implementation in vehicles due to its low implementation requirements and low power consumption in addition to its high security level against cyber-attacks.

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.

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.

Abstract In the past few years, CMOS semiconductor has been a growing and evolving technology in VLSI. However, due to the scaling issue and some other constraints like heat generation, high power consumption QCA (quantum cellular automata) emerged as an alternate and enhanced solution that provides a new technique of computing than CMOS in recent years. QCA is highly effective in implementing both Irreversible and Reversible logic, which has been shown to be incredibly efficient in terms of power consumption. A novel technique to data encryption called as SCV (select, cross, and variation) is demonstrated in this paper, which is based on ASCII to binary conversion and uses reversible logic. The data security procedure is aided by implementing SCV logic in reversible logic. Using Fredkin gate, it is built in QCA. QCADesigner tool has been used here for design and verification purposes. Total 80 cell counts and 0.14 μm2 area are required. The theoretical data and the simulation results are the same as the intended circuit. Comparison to previous QCA architectural features is featured.

The paper presents analysis of the possibility of using selected hash functions submitted for the SHA-3 competition in the SDEx encryption method. The group of these functions will include the finalists of the SHA-3 competition, i.e. BLAKE, Grøstl, JH, Keccak, Skein. The aim of the analysis is to develop more secure and faster cryptographic algorithm compared to the current version of the SDEx method with SHA- 512 and the AES algorithm. When considering the speed of algorithms, mainly the software implementation will be taken into account, as it is the most commonly used.

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

Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transmission of one-time pad encryption, which can greatly reduce the usage of channel resources. Then, a joint encryption model based on randomized AENN and a new chaotic coupling mapping is proposed. The performance analysis concludes that the encryption model possesses a huge key space and high sensitivity, and achieves the effect of one-time pad encryption. Experimental results show that this model is a high-security joint encryption model that saves secure channel resources and has the ability to resist common attacks, such as exhaustive attacks, selective plaintext attacks, and statistical attacks.

Electronic-commerce (e-commerce) has become a provider of distinctive services to individuals and companies due to the speed and flexibility of transferring orders and completing commercial deals across far and different places. However, due to the increasing attacks on penetrating transaction information or tampering with e-commerce requests, the interest in protecting this information and hiding it from tamperers has become extremely important. In addition, hacking these deals can cause a huge waste of money and resources. Moreover, large numbers of connected and disconnected networks can cause significant disruption to the built-in security measures. In this paper, we propose to design a protocol to protect transaction information based on ElGamal, advanced encryption standard (AES) and Chinese remainder theorem (CRT) techniques. In addition, our protocol ensures providing scalability with high-performance security measures. We combine these algorithms with a robust methodology that supports the balance of performance and security of the proposed protocol. An analysis of our results proves that our protocol is superior to existing security protocols.

Nowadays, cloud-based storage systems play a vital role in IoT data storage, processing, and sharing. Despite its contribution, the current cloud-based architecture may cause severe data leakage or jeopardize user privacy. Meanwhile, the cloud-based architecture heavily relies on a trusted third-party auditor (TPA) and runs in a centralized control manner. However, the TPA may not be a completely trustworthy entity, and a single point of failure might cause the centralized system to collapse. Fortunately, with the advent of blockchain technology, the decentralized storage model has gained popularity. A decentralized storage system successfully eradicates the rule of TPA, solves the problem of a single point of failure, and has many advantages over a centralized control architecture, such as low storage prices and high throughput. This study offers a blockchain-based decentralized distributed storage and sharing scheme that provides end-to-end encryption and fine-grained access control. In our proposed IoTChain model, fine-grained permission is based on attribute-based access control (A-BAC) policy by employing the Ethereum blockchain as an auditable access control layer. Smart contracts are tailored for the IoTChain model, which combines the Ethereum blockchain and the interplanetary file system (IPFS). We used an advanced encryption standard (AES) for encryption and the elliptic curve Diffie-Hellman key exchange protocol for secret key sharing between data owners and users. Also, the proof-of-work (PoW) consensus mechanism is replaced with a proof-of-authority (PoA) to minimize system transaction cost and boost system throughput. Additionally, our solution has been tested on the Ethereum official test network Rinkeby, and the results demonstrate that our approach is realistic and economical on the IoT data.

The information security has an irreplaceable position in the smart grid (SG). In order to avoid the malicious attack and ensure the information security, the cryptographic techniques are essential. This paper focuses on the encryption techniques to ensure the information security of SG. Firstly, an unusual 7-dimensional complex chaotic system (7D-CCS) combined with the cubic memristor is introduced. Besides its phase portraits, Lyapunov exponent, 0–1 test, complexity, and bifurcation diagram are investigated. Then, with the proposed 7D-CCS, we design a data encryption algorithm to ensure the encryption security. Finally, the data and monitoring images in SG are encrypted by the designed encryption scheme. Besides, the encryption performance is given in detailed. The experimental results show that the proposed encryption scheme has quite good encryption performance. Therefore, it can ensure the information security of SG.

Data access control is a crucial aspect of data management. Actors who want to share data need systems to manage consent in order to decide who can access their data. This guarantees the privacy of data, which is often sensitive. As a secure distributed ledger, the blockchain is widely used today to manage consent for data access. However, a blockchain is not ideal for storing large volumes of data due to its characteristics. Therefore, it is often coupled with off-chain systems to facilitate the storage of these kinds of data. Therefore, data located outside the blockchain require security procedures. This article proposes a securing mechanism based on data encryption to secure data in off-chain storage in blockchain-based consent systems. The protocol uses a symmetric key system, which prevents the reading of data stored outside the sphere of the blockchain by malicious actors who would have access. The mechanism’s set up allows each set of data to be encrypted with a symmetric key that is anchored in a blockchain. This key is then used by the actors who have obtained the consent of the data owner to access and read the data stored outside the blockchain.

The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

Clinical photography is an essential component of patient care in plastic surgery. The use of unsecured smartphone cameras, digital cameras, social media, instant messaging, and commercially available cloud-based storage devices threatens patients’ data safety. This paper Identifies potential risks of clinical photography and heightens awareness of safe clinical photography. Specifically, we evaluated existing risk-mitigation strategies globally, comparing them to industry standards in similar settings, and formulated a framework for developing a risk-mitigation plan for avoiding data breaches by identifying the safest methods of picture taking, transfer to storage, retrieval, and use, both within and outside the organization. Since threats evolve constantly, the framework must evolve too. Based on a literature search of both PubMed and the web (via Google) with key phrases and child terms (for PubMed), the risks and consequences of data breaches in individual processes in clinical photography are identified. Current clinical-photography practices are described. Lastly, we evaluate current risk mitigation strategies for clinical photography by examining guidelines from professional organizations, governmental agencies, and non-healthcare industries. Combining lessons learned from the steps above into a comprehensive framework that could contribute to national/international guidelines on safe clinical photography, we provide recommendations for best practice guidelines. It is imperative that best practice guidelines for the simple, safe, and secure capture, transfer, storage, and retrieval of clinical photographs be co-developed through cooperative efforts between providers, hospital administrators, clinical informaticians, IT governance structures, and national professional organizations. This would significantly safeguard patient data security and provide the privacy that patients deserve and expect.

Cloud storage service has shown its great power and wide popularity which provides fundamental support for rapid development of cloud computing. However, due to management negligence and malicious attack, there still lie enormous security incidents that lead to quantities of sensitive data leakage at cloud storage layer. From the perspective of protecting cloud data confidentiality, this paper proposed a Cloud Secure Storage Mechanism named CSSM. To avoid data breach at the storage layer, CSSM integrated data dispersion and distributed storage to realize encrypted, chucked and distributed storage. In addition, CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage. The experimental results indicate that proposed mechanism is not only suitable for ensuring the data security at storage layer from leakage, but also can store huge amount of cloud data effectively without imposing too much time overhead. For example, when users upload/download 5G sized file with CSSM, it only takes 646seconds/269seconds, which is acceptable for users.

Cryptography and steganography are combined to provide practical data security. This paper proposes integrating light-weight cryptography with improved Arabic text steganography for optimizing security applications. It uses light-weight cryptography to cope with current limited device capabilities, to provide acceptable required security. The work tests hiding encrypted secret information within Arabic stego-cover texts, using all common diacritics found naturally in the Arabic language. The study considers different challenging situations and scenarios in order to evaluate security practicality. It further carries out simulations on some short texts from the Holy Quran, taking them as standard authentic texts, that are fixed and trusted, therefore providing realistic study feedback that is worth monitoring. Our improved approach features preferred capacity and security, surpassing the best previous diacritics stego approach, showing interesting potential results for attractive enlightening exploration to come.

The old method used for examining data correctness is to recover the entire data from the cloud and then validate data reliability by scrutinizing the correctness of signatures or numeric values of the data as a whole. In modern cryptology, an essential problem to study here is to maintain the greatest benefit of the privacy of given information to perform multiple encryptions. The study shows how a decryption key is made stronger in the sense that it enables several ciphertexts to be decrypted without increasing their size. The statement of the problem lies in the fact that creating an effective public-key encryption system that supports flexible delegation is decipherable by a fixed-size decryption key that could be produced by the holder of the master-secret key. The solution to this problem is by presenting a definite type of public-key encryption known as key-aggregate cryptosystem (KAC). By using this method, users who hold master-secret key could convert a message through a public-key, and an identifier of ciphertext called “class”. This means that the ciphertexts could be further categorized into various classes. The main objective of this paper is to design a secure data storage system that provides multiple stimulating functions by which the storage system is divided and has no major authority.

Reversible cellular automation is a highly parallel system and produces enhanced throughput in its cryptographic applications. The throughput optimized security algorithm based on reversible cellular automata produces a better result in high-performance systems with many cores of CPU or GPU. We designed a throughput optimized block encryption technique using reversible cellular automata and compared its performance with other cellular automata-based algorithms. We tested its performance in both 8 core and 64 core CPU systems and the results showed an enhancement in throughput. This encryption system produced plaintext blocks that are immune to other blocks during cryptanalysis because of segmentation and the use of the different random numbers as seeds. It was built with 128 bits block encryption, but it was easily scalable to a higher block size without changing the algorithm. Each block of encryption used a cipher block chaining mode and was hence more secure and effective.

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

Smart home is an emerging form of the Internet of things (IoT), which provides a convenient and comfortable living environment for the smart home users. With the explosive growth of smart home information, people pay more attention to the privacy protection of smart home, including the choice of privacy and effective privacy protection scheme. This paper proposes a privacy protection scheme based on information hiding, the scheme guarantees the sensitive data transmitted securely. First, the smart home Real-time sensor data are classified into sensitive data and non-sensitive data through machine learning, the process can be controlled according to the user's preferences. Second, the sensitive data can be transmitted securely by ordinary channels using method of combination encryption with information hiding. Experiment results show that this scheme can help users to express their preferences to the privacy protection, changing the previous practice that users can only accept the privacy settings provided by the supplier. What's more, the scheme combines encryption with information hiding, providing a double guarantee for the sensitive data during they are transmitted on ordinary channels. Comparing to the previous practice of using encryption merely, this scheme greatly improves the security of data transmission and is more suitable for the real environment.

The commercialization of 5G has greatly promoted the development of medical Internet of Things (IoT). More medical devices connected to the Internet may further increase the communication power consumption. Meanwhile, privacy protection technique in cloud computing cannot match the rapid development of medical applications. Therefore, exploring secure, balanced and energy-efficient data transmission between medical devices and cloud servers is extremely challenging. This paper focuses on the security and energy consumption of medical electronic health record (EHR) data transmission and storage between cloud server and IoT device users. We build a secure energy-saving communication and encrypted storage model by adding secure energy-saving communication scheme and encryption algorithm to the traditional medical cloud model. Specifically, we propose a communication authentication algorithm MedGreen based on elliptic curve and bilinear pair. In the algorithm, the two communication parties can complete the key establishment and identity authentication only after one communication, which effectively balances the resource overhead of the key center and the user, and resists the Man-in-the-middle attack. Aiming at the characteristics of large repetition and high sensitivity of medical data, we present a secure data storage algorithm MedSecrecy based on Huffman compression and RC4. The algorithm not only maintains the RC4 encryption efficiency, reduces the amount of cipher text data, but also improves confidentiality, randomness and security of the key stream. Comprehensive analysis and simulations show that our system is secure, energy-saving and highly efficient for EHR.

In this paper, a secure chord protocol based method is presented to improve the latency and system storage requirements in smart metering. In the proposed approach, a secure multi-mode computation method is utilized which can reduce the time of data exchange and the memory consumption, by maintaining data security and subscribers privacy. This method can be utilized in smart metering networks based on internet of things (IoT). According to the simulated results, the proposed method incremented the amount of production capacity by 26% compared to the reference model. In addition, the average time to complete the data collection reduced by 65.5%, and the package delivery ratio of the proposed model incremented by 14.4 % in comparison with the reference model. Also, a secure mechanism based lightweight authentication was provided. These scheme needs half memory usage versus other security plan such as the EDAS algorithms.

The use of computers today is the most important and widespread means of storing, retrieving and circulating information through local and international networks and smart phones. Thus it is possible to intercept information through different networks or access to computers, whether independent or connected with the network for the purpose of viewing its contents or stealing information or to tamper with, and in this light must ensure the protection, reliability and credibility of information and preservation of the emergence of various means of protection such as the use of encryption and techniques of hiding or coverage of information. This research is based on a hybrid method between the Discrete Wavelets Translate technique (DWT) and the Chaos system based on the Least Significant Bit algorithm (LSB) to encrypt and hide a digital video file within another digital video that represents the cover by inserting the digital video file and segmenting it into a set of frames and then analyze each frame to its colors (red, green and blue) and then study the color values ​​of each frame and the process of encryption based on the equation of the logistics function (chaotic functions), and the application of the Discrete Wavelets Translate technique on the color slides of the cover frame. The hiding process is done using the Least Significant Bit algorithm based on the logistic function by calculating the series of random locations of cover area. The results showed that the hybridization of the Discrete Wavelets Translate technique and the chaos system based on the Least Significant Bit algorithm was an effective method for encrypting and hiding digital video files. After applying the work algorithms to a set of samples, the results showed consistency and compatibility in the encrypting and hiding process with the samples that was dealt with.

Data exchange over the Internet and other access channels is on the rise, leads to the insecurity of consequences. Many experiments have been conducted to investigate time-efficient and high-randomized encryption methods for the data. The latest studies, however, have still been debated because of different factors. The study outcomes do not yield completely random keys for encryption methods that are longer than this. Prominent repetition makes the processes predictable and susceptible to assaults. Furthermore, recently generated keys need recent algorithms to run at a high volume of transactional data successfully. In this article, the proposed solutions to these two critical issues are presented. In the beginning, one must use the chaotic series of events for generating keys is sufficient to obtain a high degree of randomness. Moreover, this work also proposes a novel and non-traditional validation test to determine the true randomness of the keys produced from a correlation algorithm. An approximate 100% probability of the vital phase over almost infinitely long-time intervals minimizes the algorithms’ complexity for the higher volume of data security. It is suggested that these algorithms are mainly intended for cloud-based transactions. Data volume is potentially higher and extremely changeable 3% to 4% of the improvement in data transmission time with suggested algorithms. This research has the potential to improve communication systems over ten years by unblocking decades-long bottlenecks.

The multi-source data collected by the power Internet of Things (IoT) provide the data foundation for the power big data analysis. Due to the limited computational capability and large amount of data collection terminals in power IoT, the traditional security mechanism has to be adapted to such an environment. In order to ensure the security of multi-source data in the power monitoring networks, a security system for multi-source big data in power monitoring networks based on the adaptive combined public key algorithm and an identity-based public key authentication protocol is proposed. Based on elliptic curve cryptography and combined public key authentication, the mapping value of user identification information is used to combine the information in a public and private key factor matrix to obtain the corresponding user key pair. The adaptive key fragment and combination method are designed so that the keys are generated while the status of terminals and key generation service is sensed. An identification-based public key authentication protocol is proposed for the power monitoring system where the authentication process is described step by step. Experiments are established to validate the efficiency and effectiveness of the proposed system. The results show that the proposed model demonstrates satisfying performance in key update rate, key generation quantity, data authentication time, and data security. Finally, the proposed model is experimentally implemented in a substation power IoT environment where the application architecture and security mechanism are described. The security evaluation of the experimental implementation shows that the proposed model can resist a series of attacks such as counterfeiting terminal, data eavesdropping, and tampering.

Wireless Body Area Networks (WBANs) play an important role in healthcare system by enabling medical experts to guide patients without measuring face-to-face. The unauthorized access of medical data from WBAN controller as well as the unreliable data communication may leads to risk for patients life. Currently, Chunqiang Hu et al., [1] proposed a data communication protocol by using Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for a single file. The major limitation of Chunqiang Hu et al., [1] is that as the number of files increases, CP-ABE will suffer from parameters such as message size, energy consumption and computation cost. This paper proposes a more secure and efficient data communication protocol for WBANs by using an efficient File Hierarchy CP-ABE (FH-CP-ABE). We propose a more efficient Secure and Efficient data communication protocol for WBANs by using an efficient File Hierarchy CP-ABE (FH-CP-ABE). We use the integrated access structure which is a combination of two or more access structures and with the hierarchical files are encrypted. We evaluate the performance analysis of the proposed data communication protocol in terms of message size, energy consumption, computation cost and compared with Chunqiang Hu et al., [1] while the number of files increases.

Currently, Internet of Things (IoT) as an essential infrastructure proposed for industries and different applications has been popularly applied to different domains, such as healthcare and smart farming, for helping people to do something, aiming to improve our living environments. LoRaWAN, as a Long-Range Wide Area Network specification recommended by the LoRa Alliance, is a low power and long distance communication protocol suitable for IoT environments. This protocol adopts a widely used data encryption method, i.e., Advanced Encryption Standard (AES), developed based on powerful algebra operations and multiple encryption cycles to ensure its communication security. LoRaWAN reduces communication power by setting different transmission latencies for different end-devices; however, AES does not take into account its end device's encryption power. In this paper, a high secure but low-power consumption communication scheme for the LoRaWAN, named the Secure Low Power Communication (SeLPC) method, is proposed to further reduce end-devices' data encryption power by reducing encryption cycles of AES. In the SeLPC, encryption key and D-Box update procedure is presented to enhance security level and simplify the AES encryption process so that the power consumption can be further lowered. Comparing with the traditional AES, the analysis results show that the SeLPC can minimize the encryption power up to 26.2%. The SeLPC can also resist three attacks, including known-key, replay, and eavesdropping attacks and is practically helpful for use in LoRaWAN IoT environments.

Based on degradable pH-responsive hydrogel, we report on an enhanced three-dimensional data encryption security technique in which a pH value is used for information manipulation. Featuring three types of states upon the pH value variation, namely, shrinkage, expansion and degradation, the hydrogel renders a limited pH value window as the “key” for information decryption. The pH-dependent shrinkage-to-expansion conversion of the hydrogel leads to a threshold pH value for retrieving the recorded data, whilst the degradability of the hydrogel, which can be tuned by adjusting the composition ratio of PEGDA/AAc, gives rise to a second threshold pH value for irreversibly sabotaging the retrieved data. Pre-doping silver ions in the hydrogel facilitates explicit recording and reading of binary data in forms of three-dimensional silver patterns through photoreduction and scattering, respectively, with a femtosecond laser. By accurately matching the vertical spacing of the encoded silver nanopatterns with the diffraction-limited focal depth of the decryption microscope, we can tune the pH value to encrypt and retrieve information recorded in layers and set a critical pH value to smash encoded information, which proves a highly secured 3D data encoding protocol. This strategy can effectively enrich data encryption techniques, vastly enhancing data security within unattained chemical dimensions.

Secure file transfer based upon well-designed file encryption and authorization systems expend considerable effort to protect passwords and other credentials from being stolen. Transferring and storing passwords in plaintext form leaves them at risk of exposure to attackers, eavesdroppers and spyware. In order to avoid such exposure, powerful encryption/authentication systems use various mechanisms to minimize the possibility that unencrypted credentials will be exposed, as well as be sure that any authentication data that does get transmitted and stored will be of minimal use to an attacker. In this paper we proposed a method to protect data transferring by three hybrid encryption techniques: symmetric AES algorithm used to encrypt files, asymmetric RSA used to encrypt AES password and HMAC to encrypt symmetric password and/or data to ensure a secure transmitting between server-client or client-client from verifying in-between client and server and make it hard to attack by common attacked methods.

Different applications require different level of security where the scarce of resource plays effective role. In order to protect the private medical data in the internet of things (IoT) field, the search for the optimal encryption algorithm is a must. Electronic sensors are used to collect medical data from the patient's body acquiring its transmission to the healthcare system securely. It is essential to ensure trust and data secrecy from the starting point-sensors throughout the medical treatment to prevent any unauthorized access or unneeded interruption. Thus, data encryption from the beginning sensors is necessary but facing all limitations in computing complexity, power consumption and communication bandwidth, where the normal available crypto-algorithms are considered heavyweight is completely unpractical. This work study several realistic lightweight encryption algorithms suitable for IoTmedical systems. The paper outlines a comparison between ten applicable cryptographic algorithms resulting fair analysis in terms of memory utilization and speed. The investigation deduces the optimal candidate algorithm for the proposed health care system considering the balance between the optimal requirement and the future threats.

For data encryption storage requirement of safety monitoring and control system in Technology schemes of upgrading of coal mine safety monitoring and control system(exposure draft), database encryption technology was researched, and performances of four data encryption algorithms including DES, RSA, homomorphism and binary serialization were tested and compared. A data encryption scheme suitable for coal mine safety monitoring and control system was proposed, which used different data encryption algorithm for different field type. The scheme realizes safe and quick data encryption storage for coal mine safety monitoring and control system.

A ＂big clouds audited by a small cloud＂ scheme was proposed,by introducing an independent trusted third party (TTP) dealing with run-time data collection,verification,audit and evaluation remotely,in a continuous and data-driven model,compared with traditionally certification based audit.The TTP mainly adopts data flow visualization,data monitoring and encryption to protect the rights of users.It provides the basis for users to choose a trusted cloud platform and for cloud platform to prove own trusted credentials.In-depth study,the following key technologies were broken through:1) the introduction of an independent trusted third party as an intermediate layer between cloud platform and users as well as administrators; 2) continuous,real-time remote data collection and data analysis; 3) strong non-intrusive evidence gathering.

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

This article presents an alternative for resolving the existing vulnerability of systems that implement masking by means of chaotic synchronization. This system avoids detection of the parameters used as the cipher key by an attacker on implementing encryption by means of synchronized chaotic-phase masks of the Rössler oscillator, for encoding and establishing synchronization among transmitterreceiver devices. In addition, it employs two ciphering keys: the first, with a recommended length of 2,048 characters, and the second, which is used as an initial value. Both keys are employed for continual modification of one of the oscillator’s parameters. This strengthens the security system and avoiding an attacker from obtaining the oscillator’s parametric values by calculating the least average synchronization error. The use of the system developed provides a cipher, which is resistant to statistical attacks. In addition, our system validates the data of the transmitter device (username, password, etc.) in order to authorize transmission.

Big data is and will be used more in the future as a tool for everything that happens both online and offline. Of course , online is a real hobbit, Big Data is found in this medium , offering many advantages , being a real help for all consumers. In this paper we talked about Big Data as being a plus in developing new applications, by gathering useful information about the users and their behaviour.We've also presented the key aspects of real-time monitoring and the architecture principles of this technology. The most important benefit brought to this paper is presented in the cloud section.

Applying the data encryption method used in conventional personal computers (PC) to wireless communication devices such as IoT is not trivial. Because IoT equipment is extremely slow in transferring data and has a small hardware area compared with PCs, it is difficult to transfer large data and perform complicated operations. In particular, it is difficult to apply the RSA encryption method to wireless communication devices because it guarantees the stability of data encryption because it is difficult to factor extremely large prime numbers. Furthermore, it has become even more difficult to apply the RSA encryption method to IoT devices as a paper recently published indicated that it enables rapid fractional decomposition when using RSA encryption with a prime number generated through several pseudo-random number generators. To compensate for the disadvantages of RSA encryption, we propose a method that significantly reduces the encryption key using a true prime random number generator (TPRNG), which generates a prime number that cannot be predicted through bio-signals, and a disposable RSA encryption key. TPRNG has been verified by the National Institute of Standards and Technology. The NIST test and an RSA algorithm are implemented through Verilog.

A large amount of data information will be generated during the construction and operation of the power system, and once these data are leaked, it will undoubtedly threaten the information security of power users, which will bring avoidable economic losses to power companies and users. Therefore, many electric power companies are paying more and more attention to data security, and data encryption technology solves this problem well. And this article has launched a detailed analysis on the application of data encryption technology in the construction of electric power information. At the same time, it has carried out an in-depth discussion with the practical application of the two-layer encryption method in the informationization of electric power enterprises. I hope the research results of this article It can provide a certain reference for the information security management of the power system.