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Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

It is now more crucial than ever to provide data security, as the goals and capabilities of eavesdroppers are constantly changing. Therefore, different developers are creating cipher systems that employ various innovative techniques. Standard ciphers, such as DES and AES, use substitution boxes to ensure secure encryption and decryption of data. The substitution box (S-box) is a core module used in modern ciphers to secure data. This study introduces an efficient and straightforward method that leverages a Residue Number System (RNS) to construct an S-box. Furthermore, the AES algorithm uses the generated S-box to encrypt digital images. Parameters such as entropy, NPCR, and UACI effectively measure the security of the proposed method. The results of the performance and comparison study confirm that the proposed S-box outperforms existing methods, establishing it as a strong candidate for cryptographic use in various image security applications. [ABSTRACT FROM AUTHOR]

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]

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. [ABSTRACT FROM AUTHOR]

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products' biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method's reliability, security, and efficiency. The experimental results show that this research scheme's initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry. [ABSTRACT FROM AUTHOR]

Security of modern classical data encryption often relies on computationally hard problems, which can be trivialized with the advent of quantum computers. A potential remedy for this is quantum communication which takes advantage of the laws of quantum physics to provide secure exchange of information. Here, quantum key distribution (QKD) represents a powerful tool, allowing for unconditionally secure quantum communication between remote parties. At the same time, microwave quantum communication is set to play an important role in future quantum networks because of its natural frequency compatibility with superconducting quantum processors and modern near-distance communication standards. To this end, we present an experimental realization of a continuous-variable QKD protocol based on propagating displaced squeezed microwave states. We use superconducting parametric devices for generation and single-shot quadrature detection of these states. We demonstrate unconditional security in our experimental microwave QKD setting. The security performance is shown to be improved by adding finite trusted noise on the preparation side. Our results indicate feasibility of secure microwave quantum communication with the currently available technology in both open-air (up to ~ 80 m) and cryogenic (over 1000 m) conditions. Microwave-based quantum key distribution would allow a secure exchange of information in superconducting local area networks and, potentially beyond, in open-air conditions. Here, the authors provide a proof-of-principle implementation within a cryogenic environment, based on propagating displaced squeezed microwave states. [ABSTRACT FROM AUTHOR]

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security. [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]

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. [ABSTRACT FROM AUTHOR]

With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs. [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 today's world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob's and Eve's accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob's neural network effectively secured the messages from Eve's neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection. [ABSTRACT FROM AUTHOR]

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments. [ABSTRACT FROM AUTHOR]

This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use. [ABSTRACT FROM AUTHOR]

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner. Homomorphic encryption contributes to eliminating security issues during the data communication for edge computing. Here, the authors propose a 1-trasistor−1-memristor array as a homomorphic encryption hardware prototype. By leveraging the stochastic set-switching behavior and analog non-volatile properties of the memristor, the complete homomorphic communication process is demonstrated. [ABSTRACT FROM AUTHOR]

Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user's device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol's effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. [ABSTRACT FROM AUTHOR]

Edge computing serves as a critical intermediary for secure data exchange between IoT devices and data centres in healthcare, where the protection of sensitive patient data is paramount. This study presents UdesMec, a comprehensive edge computing framework designed for efficient and secure data storage and processing in such contexts. Focusing on the challenge of implementing robust data security models in the presence of limited resources at edge nodes, UdesMec employs a unified encryption approach that is particularly suited for expert knowledge-based prediction of complex critical medical events. Using secret sharing and homomorphic encryption, it ensures the confidentiality and integrity of sensitive patient data transmitted by IoT devices, while enabling efficient computation of prediction algorithms on a cloud server. The experimental evaluation confirms the strong encryption performance and the ease of use of UdesMec, positioning it as a promising solution for the secure and reliable prediction of complex critical medical events in healthcare systems based on edge computing. [ABSTRACT FROM AUTHOR]

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower–upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NC wat 1 / NC wat 2 of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2 100 , which provides a high protection degree against brute-force attacks. The proposed scheme achieves better robustness, imperceptibility, and security performance than previous schemes. Our method can reduce time costs by 98% (91% on average), which results in lower time costs than other conventional encryption techniques. [ABSTRACT FROM AUTHOR]

• A new optical image encryption and authentication scheme is proposed with the computational ghost imaging. • The problem of inherent linear defect and key management in computational ghost imaging encryption system is solved. • The dual-channel encryption method provides multi-layer protection for the plaintext images. Considering the necessity of identity authentication in most scenarios, a dual-channel (digital channel and optical channel) image encryption and authentication algorithm with computational ghost imaging (CGI) is proposed. The entire encryption process is governed by the pseudo-random sequences obtained by the 4D chaotic system and the Logistic map. During the digital channel encryption, the confusion and diffusion of the plaintext image are achieved by random scrambling and DNA computing, respectively. To alleviate the burden of key transmission and storage in the CGI encryption system, the speckle measurement matrices are constructed with the pseudo-random numbers obtained by the Logistic map. Additionally, a nonlinear encryption operation is carried out to compensate the linear vulnerability of CGI encryption. In the optical encryption channel, the authentication image is first encrypted with the CGI method, followed by the secondary encryption of the intensity values recorded by a bucket detector to produce an intermediate ciphertext. The final encryption image is generated by merging the intermediate ciphertexts from both encryption channels. It is demonstrated that the proposed optical image encryption and authentication scheme with CGI provides satisfactory encryption and decryption effects and can effectively counteract the common attacks. Moreover, the scheme holds promising application prospects due to its authentication capability. [ABSTRACT FROM AUTHOR]

Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext. [ABSTRACT FROM AUTHOR]

A supply chain (SC) encompasses a network of businesses, individuals, events, data, and resources orchestrating the movement of goods or services from suppliers to customers. Leveraging a blockchain-based platform, smart contracts play a pivotal role in aligning business logic and tracking progress within supply chain activities. Employing two distinct ledgers, namely Hyperledger and Ethereum, introduces challenges in handling the escalating volume of data and addressing the technical expertise gap related to supply chain management (SCM) tools in blockchain technology. Within the domain of blockchain-based SCM, the growing volume of data activities introduces challenges in the efficient regulation of data flow and the assurance of privacy. To tackle these challenges, a straightforward approach is recommended to manage data growth and thwart unauthorized entries or spam attempts within blockchain ledgers. The proposed technique focuses on validating hashes to ensure blockchain integrity. Emphasizing the authentication of sensitive data on the blockchain to bolster SCM, this approach compels applications to shoulder increased accountability. The suggested technique involves converting all data into textual format, implementing code encryption, and establishing permission-based access control. This strategy aims to address inherent weaknesses in blockchain within SCM. The results demonstrate the efficacy of the proposed technique in providing security and privacy for various types of data within SCM. Overall, the approach enhances the robustness of blockchain-based SCM, offering a comprehensive solution to navigate evolving challenges in data management and privacy assurance. [ABSTRACT FROM AUTHOR]

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient's digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application's response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application's potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals. [ABSTRACT FROM AUTHOR]

In order to solve the problem of communication data theft in conventional network communication data transmission methods and ensure the security of network communication data transmission, it is necessary to design new network communication data security transmission methods based on cloud computing technology, formulate network communication data security transmission agreements, construct a network communication data security transmission model based on cloud computing technology, and design a network communication data security transmission scheme, implement secure transmission of network communication data. The experimental results show that after using the designed network communication data secure transmission method, the amount of stolen communication data is less than that of conventional methods. This proves that the designed network communication data secure transmission method has high transmission security, good transmission effect, and reliability, and can be used as a reference for subsequent network communication data encryption transmission. [ABSTRACT FROM AUTHOR]

This paper proposes a reversible data embedding algorithm in encrypted images of cloud storage where the embedding was performed by detecting a predictor that provides a maximum embedding rate. Initially, the scheme generates trail data which are embedded using the prediction error expansion in the encrypted training images to obtain the embedding rate of a predictor. The process is repeated for different predictors from which the predictor that offers the maximum embedding rate is estimated. Using the estimated predictor as the label the Convolutional neural network (CNN) model is trained with the encrypted images. The trained CNN model is used to estimate the best predictor that provides the maximum embedding rate. The estimation of the best predictor from the test image does not use the trail data embedding process. The evaluation of proposed reversible data hiding uses the datasets namely BossBase and BOWS-2 with the metrics such as embedding rate, SSIM, and PSNR. The proposed predictor classification was evaluated with the metrics such as classification accuracy, recall, and precision. The predictor classification provides an accuracy, recall, and precision of 92.63%, 91.73%, and 90.13% respectively. The reversible data hiding using the proposed predictor selection approach provides an embedding rate of 1.955 bpp with a PSNR and SSIM of 55.58dB and 0.9913 respectively. [ABSTRACT FROM AUTHOR]

With the rapid development of vehicle electronic communication technology, in-vehicle bus network system communicates with external electronic devices such as mobile phones and OBD II, causing in-vehicle bus networks to face severe network security threats. This study aims to explore the security scheme of in-vehicle bus networks based on a key management system to ensure the confidentiality, integrity, authenticity, and availability of vehicle communication, and innovatively propose a key management system. This key management system uses data encryption and signature algorithms based on the elliptic curve cryptographic domain, which is mainly composed of key generation and key distribution modules. By designing a key life cycle management strategy for In-Vehicle Ethernet and using the digital envelope technique, data encryption and digital signatures are combined to ensure the secure generation and distribution of keys. Experimental simulation results show that the session key negotiation speed of the proposed key management system for In-Vehicle Ethernet in this study is 1.533 ms, which improves the speed by 80.5% compared with the traditional key management system. The key management system proposed in this study improves the real-time information processing efficiency in In-Vehicle Ethernet and lays a solid foundation for the stable development of intelligent connected vehicles. [ABSTRACT FROM AUTHOR]

A method of transmission of highly confidential information in power communication network based on digital watermarking technology is proposed in order to reduce the leakage risk of power information and achieve the purpose of safe transmission of private information. This method establishes the data topology model of power communication network and connects the data receiving and sending terminals. Digital watermarking technology is used to embed digital watermarking in the information to be transmitted, encrypt the private information, and optimize the DES encryption algorithm to encrypt the information twice, so as to realize the safe transmission of information. The experimental results show that the digital watermark embedding rate, DES encryption rate, DES decryption rate and watermark extraction rate of the proposed method are all above 90 Mbps, and the file transfer time of 3965Byte is less than 5s, leakage risk rate and packet loss rate are 0.0001% and 0.006%, respectively, which effectively protects the security of high privacy information in the power communication network and prevents information leakage. [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.

Enhanced computations resulted in the evolution of cyber-physical systems (CPS). These systems are highly vulnerable to security threats. This work addresses the security concerns for a reliable CPS in the Internet of Things (IoT) through an improvised RSA with enhanced security features. The proposed method renders data throughput encryption at 11.67Kbps with an overall average execution time of 0.02s. The proposed algorithm exhibits the unique feature of being asymmetric, lightweight & enhanced security owing to randomization (average entropy of 0.98). The performance metrics of the proposed method are compared with other state-of-art techniques. This algorithm is implemented on commercially competent architecture STM32F401RET6 with cortex M4 processor running @ 84MHz. The proposed encryption method exhibits great features thus making it a potential real-world solution. [ABSTRACT FROM AUTHOR]

Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing in this paper. The proposed method achieves fne-grained access control with low computation consumption. We design Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption that reinforces attribute revocation and averts the discharge of sensitive information. Furthermore, we utilize Optimal Cache Oblivious algorithm to prevent disclosure of access patterns to hide the access patterns in cloud storage via rand pattern matching. We support updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. We perform secure data sharing to achieve higher data confidentiality and integrity. Finally, we conducted the extensive experiments in cloud and the results illustrated that our proposed BGNBA-OCO method is more efficient than related works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a camera projection approach to enhance the physical layer security of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The data are converted to the new location by the camera projection module in the encryption system, where the 5D hyperchaotic system provides the keys for the camera projection module. The simulated 16QAM CO-OFDM security system over 80 km SSMF is shown to provide a key space of about 9 × 1090 through the five-dimensional (5D) hyperchaotic system, making it impossible for eavesdroppers to obtain valid information, and the peak-to-average power ratio (PAPR) is reduced by about 0.8 dB. [ABSTRACT FROM AUTHOR]

In the face of advancing quantum computing capabilities posing significant threats to current cryptographic protocols, the need for post-quantum cryptography has become increasingly urgent. This paper presents a comprehensive analysis of the performance of various post-quantum cryptographic algorithms specifically applied to digital signatures. It focuses on the implementation and performance analysis of selected algorithms, including CRYSTALS-Dilithium, Falcon, and SPHINCS+, using the liboqs library. Performance tests reveal insights into key pair generation, file signing, and signature verification processes. Comparative tests with the well-known and popular RSA algorithm highlight the trade-offs between security and time efficiency. The results can help to select secure and efficient ciphers for specific 5G/6G services. [ABSTRACT FROM AUTHOR]

Photoluminescence (PL) colour-changing materials in response to photostimulus play an increasingly significant role in intelligent applications for their programmability. Nevertheless, current research mainly focuses on photochemical processes, with less attention to PL transformation through uniform aggregation mode adjustment. Here we show photochromic luminescence in organic crystals (e.g. dimethyl terephthalate) with PL varying from dark blue to purple, then to bright orange-red, and finally to red. This change is attributed to the emergence of clusters with red emission, which is barely achieved in single-benzene-based structures, thanks to the subtle molecular rearrangements prompted by light. Crucial to this process are the through-space electron interactions among molecules and moderate short contacts between ester groups. The irradiated crystals exhibit reversible PL transformation upon sufficient relaxation, showing promising applications in information storage and smart optoelectronic devices. This research contributes to the development of smart photochromic luminescent materials with significant PL colour transformations through molecular rearrangement. Color-changing emissions in organic crystals are often realized via photochemical reactions of their molecules. Here, the authors demonstrate reversible color change through more simple molecular rearrangements with application in encryption and data storage. [ABSTRACT FROM AUTHOR]

One of the routine exercises to manage and improve the performance and utility of the cloud is the migration or transfer of cloud data whether it is large or small. However, it is extremely challenging to protect both data privacy and integrity concurrently while moving cloud data, particularly when it is very vast. Collectively, prior works fail to offer a complete solution to these problem. Even though data encryption and steganography techniques are popular and efficient, they provide higher time and space complexities and introduce information loss. As a result, the goal of this research is to provide a chaos compression and encryption system based on chaos theory to guarantee both data privacy and integrity during the transit or migration of massive cloud data. During data transmission, the entire data are compressed using a chaotic substitution box followed by an adaptive Huffman encoding algorithms. Therefore, the input data are efficiently transformed into a non-readable form which replaces the original data, making it difficult for an unethical individual or group to determine its true sense. Our evaluation results show that the proposed chaotic technique has a maximum entropy value of 7.99, which supports its ability to provide more privacy when compared to previous techniques. It also delivers the best bits per code of 4.41, a throughput of 2.89 MB/s, and a minimal information loss percentage of 0.0011%, demonstrating its superior time, space efficiency, and ability to improve data integrity over existing methods. [ABSTRACT FROM AUTHOR]

Discrete chaotic maps are a mathematical basis for many useful applications. One of the most common is chaos-based pseudorandom number generators (PRNGs), which should be computationally cheap and controllable and possess necessary statistical properties, such as mixing and diffusion. However, chaotic PRNGs have several known shortcomings, e.g., being prone to chaos degeneration, falling in short periods, and having a relatively narrow parameter range. Therefore, it is reasonable to design novel simple chaotic maps to overcome these drawbacks. In this study, we propose a novel fractal chaotic tent map, which is a generalization of the well-known tent map with a fractal function introduced into the right-hand side. We construct and investigate a PRNG based on the proposed map, showing its high level of randomness by applying the NIST statistical test suite. The application of the proposed PRNG to the task of generating surrogate data and a surrogate testing procedure is shown. The experimental results demonstrate that our approach possesses superior accuracy in surrogate testing across three distinct signal types—linear, chaotic, and biological signals—compared to the MATLAB built-in randn() function and PRNGs based on the logistic map and the conventional tent map. Along with surrogate testing, the proposed fractal tent map can be efficiently used in chaos-based communications and data encryption tasks. [ABSTRACT FROM AUTHOR]

Medical data sharing is pivotal in enhancing accessibility and collaboration among healthcare providers, researchers, and institutions, ultimately leading to enhanced patient outcomes and more efficient healthcare delivery. However, due to the sensitive nature of medical information, ensuring both privacy and confidentiality is paramount. Access control-based data sharing methods have been explored to address these issues, but data privacy concerns still remain. Therefore, this paper proposes a secure and privacy-preserving data sharing scheme that achieves an equilibrium between data confidentiality and privacy. By leveraging key aggregate encryption and private set intersection techniques, our scheme ensures secure data sharing while protecting against the exposure of sensitive information related to data. We conduct informal and formal security analyses, including Burrow–Abadi–Needham logic and Scyther, to demonstrate its resilience against potential adversarial attacks. We also implement the execution time for cryptographic operations using multiprecision integer and a rational arithmetic cryptographic library and perform comparative analysis with existing related schemes in terms of security, computational cost, and time complexity. Our findings demonstrate a high level of security and efficiency, demonstrating that the proposed scheme contributes to the field by providing a solution that protects data privacy while enabling secure and flexible sharing of medical data. [ABSTRACT FROM AUTHOR]

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A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs. [ABSTRACT FROM AUTHOR]