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In recent years, lightweight sensors have become essential for advancing technologies, particularly in wireless sensor networks (WSNs). A persistent challenge in WSNs is maintaining continuous operation while achieving balanced energy consumption across sensor nodes. Wireless mobile energy transmitters (WMETs) provide a promising solution by wirelessly recharging sensor nodes. However, most existing approaches fail to optimize WMET charging locations, resulting in energy imbalance and reduced network coverage. Furthermore, conventional clustering systems often overlook both inter- and intra-cluster energy balancing, degrading overall network performance. Security issues, such as insufficient data encryption, further exacerbate these challenges, leaving WSNs vulnerable to attacks. To address these gaps, we propose the secure and energy-efficient inter- and intra-cluster optimization scheme (SEI2), a novel WMET-based framework that ensures balanced energy utilization among cluster heads (CHs) and member nodes (MNs) while securing data transmission. The SEI2 system incorporates UAVs to dynamically recharge sensor nodes, determining optimal charging locations within clusters to maximize energy efficiency. Additionally, robust data encryption mechanisms are applied at both the CH and base station (BS) levels to safeguard transmitted data. Key parameters considered include node energy levels, data transmission rates, optimal charging locations, and encryption overhead. Experimental evaluations demonstrate that SEI2 improves network lifetime by 35%, reduces compromised data packets by 19%, enhances coverage time by 15%, and significantly minimizes energy variance by 62% for CHs and 88% for MNs. These results highlight SEI2's potential as a comprehensive solution for extending WSN lifetimes, enhancing energy efficiency, and strengthening data security, making it particularly suitable for smart city applications. [ABSTRACT FROM AUTHOR]

Security in e-healthcare applications such as Telemedicine is crucial in safeguarding patients' sensitive data during transmission. The proposed system measures the patient's health parameters, such as body temperature and pulse rate, using LM35 and pulse sensors, respectively. The sensor data and the patient's medical image are encrypted in the Raspberry Pi 3 B + processor using Python's proposed text and medical image encryption scheme. The encrypted data is transmitted via the Thing Speak cloud and received by another Raspberry Pi at the receiver to decrypt the cipher data. The flask webserver can view the decrypted data by the doctor at the other end. This IoT implementation of secure Electronic Health Record (EHR) transmission employs text and medical image encryption schemes using a Combined Chaotic System (CCS). The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. Then, selective shuffling between the cut-off points breaks the statistical relationship between the neighbouring pixels. Finally, the intra and inter-pixel diffusion is carried out using bit permutation and bit-wise XOR operation to create a highly random cipher image. The initial seed for inter-pixel diffusion is obtained from the hash of intra-pixel diffused images to resist chosen plain text and cipher text attacks. The efficiency of the developed medical image encryption algorithm is tested against various attack analyses. The results and the security analyses validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission. [ABSTRACT FROM AUTHOR]

In various organizations and enterprises, data masking is used to store sensitive data efficiently and securely. The data encryption and secretsharing-based data deploying strategies secure privacy of subtle attributes but not secrecy. To solve this problem, the novel snowflake schema with the hash map using secure hash algorithm-256 (SHA-256) is proposed for the data masking. SHA-256 approach combines data masking by secret sharing for relational databases to secure both privacy as well as the confidentiality of secret employee data. The data masking approach supports preserving and protecting the privacy of sensitive and complex employee data. The data masking is developed on selected database fields to cover the sensitive data in the set of query outcomes. The proposed method embeds one or multiple secret attributes about multiple cover attributes in a similar relational database. The proposed method is validated through different performance metrics such as peak signal-to-noise ratio (PSNR) and error rate (ER) and it achieves the values of 50.084dB and 0.0281 when compared to the existing methods like Huffman-based lossless image coding and quad-tree partitioning and integer wavelet transform (IWT). [ABSTRACT FROM AUTHOR]

As more and more services and solutions are utilizing digital data, it becomes imperative to provide strong security mechanisms to safeguard sensitive content with a rapidly growing number of users. This is especially important for users who are not as techie, and thus do not have the knowledge or skill to implement their own protections. To this effect, the present paper proposes a new model for image validation at the receiver side. This model works on low bandwidth channel and provides same level of security as end-to-end encryption. The security of the images is realized by using elliptic curve cryptography (ECC), while the segmentation and discrete wavelet transform (DWT) cooperate for achieving both transmission efficiency and preservation of individual pixel data. The model utilizes watermark embedding and a butterfly selection method for image authentication. This involves a cognitive decision step thereby ensuring the highest possible effectiveness for integrity verification. Experiments on a standard image dataset show that the proposed model is able to be much more robust under different attack vectors. [ABSTRACT FROM AUTHOR]

[Display omitted] Flexible thermal-responsive encryption devices are widely employed in information encryption and anti-counterfeiting due to their cost-effectiveness and dynamic data encryption and decryption capabilities. However, most current devices are limited to a single layer of encryption, resulting in restricted decryption methods and storage capacity, as well as reliance on external heating. In this study, we integrate multiple layers of encryption within a single device and introduce self-heating thermochromic technology along with infrared thermal imaging encryption to establish a novel concept of a multilayer flexible encryption system. By combining infrared encryption and thermochromic encryption in three-dimensional space enhances the difficulty level for decryption while achieving high storage capacity for information. The internally integrated conductive heating layer within the multilayer structure facilitates rapid and adjustable heating for thermochromic patterns, eliminating the need for external heat sources. Furthermore, we employ a low-cost customizable multi-material integrated 3D printing process for manufacturing multilayer flexible encryption devices. This research presents an innovative solution for designing and fabricating high-density multilevel flexible encryption devices. [ABSTRACT FROM AUTHOR]

Images are important information carriers in our lives, and images should be secure when transmitted and stored. Image encryption algorithms based on chaos theory emerge in endlessly. Based on previous various chaotic image fast encryption algorithms, this paper proposes a color image sector fast encryption algorithm based on one-dimensional composite sinusoidal chaotic mapping. The main purpose of this algorithm is to improve the encryption and decryption speed of color images and improve the efficiency of image encryption in the big data era. First, four basic chaos maps are combined in pairs and added with sine operations. Six one-dimensional composite sinusoidal chaos maps (CSCM) were obtained. Secondly, select the two best chaotic mappings LCS and SCS. The randomness of these two chaotic mappings was verified through Lyapunov index and NIST SP 800–22 randomness tests. Thirdly, the encryption process is carried out according to the shape of a traditional Chinese fan, and the diffusion and scrambling of each pixel of the image are performed in parallel. This greatly improves encryption speed. When diffusing, changing the value of one pixel can affect the values of multiple subsequent pixels. When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. The key space reached 2192, the average information entropy was 7.9994, the average NPCR was 99.6172, and the average UACI was 33.4646. The algorithm can also resist some common attacks and accidents, such as exhaustion attack, differential attack, noise attack, information loss and so on. [ABSTRACT FROM AUTHOR]

The rise of IoT has also brought technological developments and threats. Edge computing has become a vital solution for most networks and demands resource-constrained operations. This work presents a lightweight data encryption scheme based on the Sierpinski Triangle Fractal, Chaotic Logistic map and their FPGA implementation on Intel Cyclone IV E. The size of the fractal geometry emerges from the number of bits in the input. Hence, this algorithm is an input-aware cryptosystem that simultaneously handles all input bits using rotation, random data fitting and diffusion. Further, round keys are supplied through the logistic map, and three incarnations of their FPGA implementations were investigated against randomness. Worst-case inputs have been taken to evaluate the design, and the results are validated through standard metrics. Round keys and cipher data have passed the NIST SP 800–22 test suites, thus evidence of the randomness. The proposed cryptosystem consumes only 944 logic elements (0.8%) and cipher 27/128/256 bits of data concurrently on Cyclone IV E FPGA. Encryption time has been calculated as 400 ns for 50 MHz of operating clock. [ABSTRACT FROM AUTHOR]

The demand for fine-grained perception of electricity usage information in the new power system is continuously increasing, making it a challenge to address potential unauthorized data access while ensuring channel security. This paper addresses privacy in power systems requiring efficient source-load interactions by introducing a novel data compression synchronous encryption algorithm within a compressed sensing framework. Our proposed algorithm uses a ternary Logistic-Tent chaotic system for generating a chaotic measurement matrix, allowing simultaneous data compression and encryption of user-side voltage and current data. This mitigates high-frequency sampling overload and ensures data confidentiality. The implementation of a joint random model at both compression and reconstruction stages eliminates the need for key transmission, reducing management costs and leakage risks. The proposed algorithm was validated using the PLAID dataset, demonstrating that the time required for a single encryption-decryption operation can be reduced by up to 81.99% compared to the asymmetric RSA algorithm. Additionally, compared to the symmetric AES algorithm, the proposed method significantly enhances confidentiality. [ABSTRACT FROM AUTHOR]

The increasing demand for secure and efficient encryption algorithms has intensified the exploration of alternative cryptographic solutions, including biologically inspired systems like cellular automata. This study presents a symmetric block encryption design based on multiple reversible cellular automata (RCAs) that can assure both computational efficiency and reliable restoration of original data. The encryption key, with a length of 224 bits, is composed of specific rules used by the four distinct RCAs: three with radius-2 neighborhoods and one with a radius-3 neighborhood. By dividing plaintext into 128-bit blocks, the algorithm performs iterative transformations over multiple rounds. Each round includes forward or backward evolution steps, along with dynamically computed shift values and reversible transformations to securely encrypt or decrypt data. The encryption process concludes with an additional layer of security by encrypting the final RCA configurations, further protecting against potential attacks on the encrypted data. Additionally, the 224-bit key length provides robust resistance against brute force attacks. Testing and analysis were performed using a custom-developed software (version 1.0) application, which helped demonstrate the algorithm's robustness, encryption accuracy, and ability to maintain data integrity. [ABSTRACT FROM AUTHOR]

Encryption and decryption of data with very low latency and high energy efficiency is desirable in almost every application that deals with sensitive data. The advanced encryption standard (AES) is a widely adopted algorithm in symmetric key cryptography with numerous efficient implementations. Nonetheless, in scenarios involving extensive data processing, the primary limitations on performance and efficiency arise from data movement between memory and the processor, rather than data processing itself. In this article, we present a novel in-memory computing (IMC) approach for AES encryption and key-expansion, and experimentally validate it on an IMC prototype chip based on phase-change memory (PCM) technology. We leverage operators stored in PCM crossbar arrays to achieve the flexibility to tune performance at runtime based on the amount of free storage available in the memory system. In addition, we introduce a method for parallel in-memory polynomial modular multiplication and evaluate the potential of intrinsic stochastic properties of PCM devices for random key generation. We show how to further improve efficiency with minimal additional auxiliary circuitry. To evaluate the performance within a custom-built large-scale in-memory AES system, we design and implement a cycle-accurate simulator that integrates parameters from Spice simulations for detailed latency and energy consumption analysis of the AES algorithm. Our evaluations indicate that our IMC-based AES approach outperforms state-of-the-art methods, achieving speed factor improvements of up to 19.7 at equivalent energy efficiency. This article is part of the theme issue 'Emerging technologies for future secure computing platforms'. [ABSTRACT FROM AUTHOR]

Internal auditing demands innovative and secure solutions in today's business environment, with increasing competitive pressure and frequent occurrences of risky and illegal behaviours. Blockchain along with secure databases like encryption improves internal audit security through immutability and transparency. Hence integrating blockchain with homomorphic encryption and multi-factor authentication improves privacy and mitigates computational overhead. Recently, blockchain applications for internal audits in the enterprise sector are still emerging. Thus, blockchain technology in auditing provides the benefits of enhanced transparency and immutability in data processing, which can establish new solutions for internal auditing but still lacks encryption techniques. The research proposed a framework called "BlockCryptoAudit" to enhance internal audit processes through cryptographic encryption methods and blockchain technology, ensuring secure and transparent audit operations. The proposed approach integrates an additive homomorphic Paillier encryption scheme with blockchain to create a safe and tamper-resident audit trail. Utilizing homomorphic Paillier encryption, BlockCryptoAudit ensures that computations may be performed on encrypted audit data while safeguarding data privacy. The applied blockchain hyperledger component guarantees the immutability and transparency of encrypted audit records, resulting in a decentralized and tamper-resistant record. By limiting data accessibility to authorized individuals based on specified responsibilities, role-based access restrictions handled using smart contracts further strengthen security. The study protects audit data's security and confidentiality by encrypting it and putting it on a blockchain. The study compares the proposed BlockCryptoAudit with models like B-OAP, BSE-DF, and EG-FLB regarding risk mitigation, audit quality, security overhead, and audit trail effectiveness. With little security overhead, BlockCryptoAudit beats out B-OAP, BSE-DF, and EG-FLB in terms of risk mitigation (98%) and audit quality (99%). It is an effective way to improve internal audit processes and guarantee data integrity due to its high performance. [ABSTRACT FROM AUTHOR]

In the digital era, ransomware attacks have become a significant threat to security systems, especially in cloud computing environments. These attacks encrypt victim data and demand ransom, causing considerable financial and operational losses. This Study aims to develop a cryptography-based early detection system for ransomware attacks to protect data in cloud environments. Using the Systematic Literature Review (SLR) approach, this Study analyzes literature related to ransomware attacks, cryptographic algorithms, and cloud security. Data are obtained from indexed journals, books, and conferences. The Study's results showed that the implementation of cryptographic algorithms, such as Advanced Encryption Standard (AES), can improve the efficiency and effectiveness of ransomware detection. This system managed to reduce detection time by 48.28%, increase the success rate of data protection from 60% to 95%, and almost double the amount of data protected. This implementation strengthens data security, minimizes the impact of ransomware, and ensures the continuity of cloud user operations. The implications of this Study support the existing literature on the importance of cryptography in mitigating digital security threats while providing practical guidance for organizations in adopting this technology. Further research is recommended to integrate cryptographic algorithms with technologies such as blockchain to increase the scale and complexity of data protection in a broader cloud environment. [ABSTRACT FROM AUTHOR]

This study examines the emerging role of messaging apps and end-to-end encryption in news consumption patterns across the European Union. Using data from the Flash Eurobarometer 3153 "Media and News Survey 2023", we employed K-Means cluster analysis to identify five distinct news consumer profiles. Our findings reveal that while messaging apps are used by 15% of EU residents for news consumption, their adoption varies significantly across demographic groups and regions. Notably, omnivorous news consumers show the highest usage (61%) and trust in these platforms, indicating a complementary role to traditional news sources. The study highlights a generational divide, with younger users and those still in education showing a stronger preference for messaging apps. Surprisingly, individuals without formal education also demonstrate high usage, challenging assumptions about the digital divide. This research offers updated, large-scale information on the evolving European news ecosystem, where private, encrypted channels are gaining importance alongside public platforms. Our findings have significant implications for media strategies, policymaking, and understanding the future of news dissemination in an increasingly digital and privacy-conscious Europe. [ABSTRACT FROM AUTHOR]

With the development and application of the Internet of Things (IoT), the volume of data generated daily by IoT devices is growing exponentially. These IoT devices, such as smart wearable devices, produce data containing sensitive personal information. However, since IoT devices and users often operate in untrusted external environments, their encrypted data remain vulnerable to potential privacy leaks and security threats from malicious coercion. Additionally, access control and management of these data remain critical issues. To address these challenges, this paper proposes a novel coercion-resistant ciphertext-policy attribute-based encryption scheme. The scheme leverages chameleon hashing to enhance deniable encryption, achieving coercion resistance, thereby enabling IoT data to resist coercion attacks. Moreover, the scheme employs attribute-based encryption to secure IoT data, enabling fine-grained access control and dynamic user access management, providing a secure and flexible solution for vast IoT data. We construct the scheme on a composite order bilinear group and provide formal proofs for its coercion resistance, correctness, and security. Finally, through experimental comparisons, we demonstrate the efficiency and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

We develop a new scattering-based framework for the holographic encryption of analog and digital signals. The proposed methodology, termed 'differential sensing', involves encryption of a wavefield image by means of two hard-to-guess, complex and random scattering media, namely, a background and a total (background plus scatterer) medium. Unlike prior developments in this area, not one but two scattering media are adopted for scrambling of the probing wavefields (as encoded, e.g. in a suitable ciphertext hologram) and, consequently, this method offers enhanced security. In addition, while prior works have addressed methods based on physical imaging in the encryption phase followed by computational imaging in the decryption stage, we examine the complementary modality wherein encryption is done computationally while decryption is done analogically, i.e. via the materialization of the required physical imaging system comprising the ciphertext hologram and the two unique (background and total) media. The practical feasibility of the proposed differential sensing approach is examined with the help of computer simulations incorporating multiple scattering. The advantages of this method relative to the conventional single-medium approach are discussed for both analog and digital signals. The paper also develops algorithms for the required in situ holography as well as a new wavefield-nulling-based approach for scattering-based encryption with envisioned applications in real-time customer validation and secure communication. [ABSTRACT FROM AUTHOR]

This paper addressed the challenges of trust issues, asymmetric contribution and benefit relationships, and data security sharing in crowdsourcing by proposing an RPC-Chain, a crowdsourcing model chain based on a reputation model and proxy re-encryption (PRE). RPC-Chain abstracted the transaction process of the crowdsourcing model based on Hyperledger Fabric. Firstly, it improved the reputation mechanism to enhance the overall efficiency and fairness of the system. Secondly, it introduced proxy re-encryption to ensure secure data sharing services. Finally, it built a crowdsourcing blockchain simulation platform using Fabric, and conducted related tests. The experiments demonstrate that the proposed scheme shows certain advantages in model design, secure data encryption sharing, and the incentive effect of the reputation model. [ABSTRACT FROM AUTHOR]

A secure IoT system can provide end-to-end encryption, secure communication protocols, mechanisms for access control, and intrusion detection and prevention systems to protect against various cyber threats. This paper presents a novel method to guarantee the secured and privateness of information in healthcare system that is Internet of Things based. This proposed method, called optimal secure and lightweight attribute-based encryption (OSL-ABE), is designed to provide an efficient and effective solution for secure data storage in the blockchain. By leveraging ABE, this method enables fine-grained access control over data, ensuring that only authorized users with specific attributes can access the data. Current ABE systems, including those used in IoT applications, often neglect privacy protection, during the key generation phase which is a significant threat to confidentiality of industrial secrets. Therefore, an optimal key generation method is proposed using modified sandpiper optimization (MSO) algorithm to ensure privacy preservation. In addition, it is incorporated with an enhanced gravitational search (EGS) algorithm in this proposed method to facilitate secure key revocations. It uses a single short broadcast message, which helps to maintain the IoT systems with confidentiality and integrity. Based on the simulation results, NPCR and UACI results exhibit 99.76% and 34%, respectively, and the entropy value is also obtained with a safe value of 8 which can withstand brute force attacks; it is found that the OSL-ABE method provides a comprehensive and effective solution for ensuring data security, preserving privacy, and facilitating secure key revocations in blockchain-enabled IoT-based healthcare systems. [ABSTRACT FROM AUTHOR]

The problem of data security in EHR is deeply concerning, as well as the methods used in session, feature, service, rule, and access restriction models. However, they fail to achieve higher security performance, which degrades the trust of data owners. To handle this issue, an efficient Adaptive Feature Centric Polynomial (AFCP) data security model is described here. The proposed method can be adapted to enforce security on any kind of data. The AFCP scheme classifies the features of EHR data under different categories based on their importance in being identified from the data taxonomy. By maintaining different categories of data encryption schemes and keys, the model selects a specific key for a unique feature with the use of the polynomial function. The method is designed to choose a dynamic polynomial function in the form of m(x) n, where the values of m and n are selected in a dynamic way. The method generates a blockchain according to the feature values and adapts the cipher text generated by applying a polynomial function to data encryption. The same has been reversed to produce the original EHR data by reversing the operation. The method enforces the Healthy Trust Access Restriction scheme in restricting malicious access. By adapting the AFCP model, the security performance is improved by up to 98%, and access restriction performance is improved by up to 97%. The proposed method increases the access restriction performance in the ratio of 19%, 16%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. Similarly, security performance is increased by 17% 13%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. [ABSTRACT FROM AUTHOR]

The widespread adoption of autonomous driving technology heavily relies on data acquisition and processing, which, while providing an intelligent experience for users, also raises concerns about data security, personal privacy, and data exploitation. The data security of autonomous driving faces challenges from three aspects: technology, law, and social ethics. Thus, this article adopts interdisciplinary research methods to identify these challenges and provide solutions from diverse disciplinary perspectives. (a) Technologically, issues such as data leakage, storage vulnerabilities, and the risk of re-identifying anonymous data persist; (b) legally, there is an urgent need to clarify the responsible parties and address issues related to outdated data security legislation and legal conflicts arising from cross-border data flows; (c) socially and ethically, the risks of data misuse and the emergence of exploitative contracts have triggered public concerns about data privacy. To address these challenges, this article proposes technical countermeasures such as utilizing diverse Privacy Enhancing Technologies (PETs) to enhance data anonymity, optimizing data encryption techniques, and reinforcing data monitoring and access control management. Legal measures should include establishing a comprehensive data security protection framework, clarifying accountability, and developing standards for the classification and grading of autonomous vehicle data. In the field of social ethics, emphasis is placed on safeguarding the public's right to know, establishing a transparent system for data use, offering an alternative "data security" solution that allows users to choose between heightened privacy protection and enhanced personalized services, and also advocating ethical data utilization and technological development. By implementing these comprehensive strategies, we aim to establish a secure and barrier-free data protection system for autonomous driving, thereby laying a solid foundation for the widespread adoption of autonomous driving technology. [ABSTRACT FROM AUTHOR]

The use of cloud computing for storing and processing cyber data via the internet has grown widespread in the field of cyber security. Ensuring the secure sharing of cyber data, including texts, images, audio, and video, over the cloud is paramount. However, cloud computing encounters significant challenges concerning cyber data security, authentication, and privacy. The prevailing approaches to data security encounter challenges such as the generation of intricate keys, intensive computation for large keys, and susceptibility to attacks by intruders. Scalability presents its own set of obstacles in maintaining cyber data privacy and ensuring secure communication. A significant privacy concern arises from the frequent changes in membership and data sharing among multiple owners. This paper introduces a novel approach to secure data sharing within data centres by proposing a scheme that employs Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and visual steganography for encoding and decoding. MK-VOTP is used for data encryption, facilitating data owners' encryption via utilising their identity with supplementary security features. Subsequently, the encrypted data is kept in the cloud, guaranteeing heightened security protocols. Visual steganography is employed for further authentication purposes. Decrypting the original data requires users who fulfil the encrypted properties. This increases security and reduces critical size, allowing several authenticated owners to share data without conflicts. Combining all shares recreates the original picture. The approach described in the paper sounds innovative and addresses several key challenges in ensuring secure data sharing within data centers, particularly in the context of cloud computing. The proposed scheme main components are Private Key Dynamic Visual Cryptography (PK-DVC), Multiple Key Encryption Visual One-Time Pad (MK-VOTP), and Visual Steganography. These three components plays important role in further authenticating the encrypted data or providing additional security measures beyond encryption. By combining these techniques, the proposed scheme aims to provide robust security for sharing cyber data in cloud environments. The use of dynamic keys, multiple encryption layers, and steganography can enhance data security, making it challenging for intruders to access or decipher sensitive information. [ABSTRACT FROM AUTHOR]

In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain's role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry. [ABSTRACT FROM AUTHOR]

[Display omitted] • Intellectual Disability (ID) requires secure storage and sharing of sensitive data. • An Adaptive Encryption Algorithm combines block scrambling, 8D hyperchaotic system and DNA coding to resist plain text, differential, brute force and cropping attacks. • Integrating Integer Wavelet Transform and DNA sequences enhances both security and computational efficiency. • Strong potential in biomedical security, medical imaging, financial transactions, and confidential communications. Intellectual disability (ID) is a neurodevelopmental disease characterized by significant intellectual and adaptive functioning impairments. Continuous monitoring and data collection are crucial for managing this condition. To collect patient's skin temperature data, doctors and caretakers often use thermal IR imaging, and they must securely share this information. To address privacy concerns and ensure robust encryption during data transmission, this study introduces an adaptive encryption algorithm that operates in both frequency and spatial domains. The algorithm counteracts various security threats, including plain text attacks, differential attacks, brute force attacks, and cropping attacks. The core of this research is the 8D Hyperchaotic DNA Encryption Algorithm, which enhances security by combining block scrambling with DNA coding. Security is further optimized through integrating the Integer Wavelet Transform (IWT) in the frequency domain and a DNA sequence in the spatial domain. The encryption process aims to balance security and computational efficiency, ensuring reliable performance. Extensive testing and validation against multiple types of attacks show the algorithm's effectiveness and reliability in practical applications. Experimental results show that the proposed system significantly enhances security against various attacks, thus setting a higher standard for encryption in biomedical image and signal processing. This algorithm shows potential for broader applications beyond intellectual disability care, including secure data transmission in medical imaging, financial transactions, and confidential communications, because of its robust encryption capabilities and adaptability to different data types. [ABSTRACT FROM AUTHOR]

[Display omitted] • Inspired by the dynamic color change exhibited by chameleons, a series of polyurethane films are reported. • The Dihydroxynaphthalene −modified films exhibit tunable fluorescence under pH changes. • The Diaminonaphthalene-modified films offer photochromic and photobleaching properties. • These films, with integrated self-healing attributes, realize high-security "burn-after-reading" encryption functions. Improving the decryption dimensionality and preventing ciphertext leakage are key challenges in encryption. To overcome these hindrances, the development of a multifunctional acrylate-modified waterborne polyurethane (POU) for advanced encryption systems with "burn-after-reading" and multidimensional coding capabilities—inspired by the rapid, reversible color changes of chameleons—is reported herein. Naphthalene dyes with different functional groups and positional isomers are incorporated into POU, yielding photoresponsive films with stable fluorescence and rapid photobleaching features, mimicking chameleon-like dynamic color transitions. Secret information is written under ultraviolet light and erased after 3 min of overexposure, achieving rapid writing and secure data destruction, thereby enabling burn-after-reading functionality. The dynamic oxime–carbamate bonds in the POU films provide self-healing properties, achieving a mechanical property recovery rate of ∼ 90 %. These films possess ideal mechanical characteristics and self-healing capabilities, enabling the creation of three-dimensional encryption modules. Additionally, the POU emulsion functions as an adhesive for label applications, ensuring secure information storage and transfer. The multifunctional polyurethane reported herein holds significant potential for applications in optical data storage, high-security encryption, and anticounterfeiting. [ABSTRACT FROM AUTHOR]

• Two novel luminogens were constructed based on isomerization and CT strategy. • Two luminogens showed high-contrast RTP. • Polymer matrix with excellent flexibility and long-lived RTP was achieved. • FRET efficiency and RTP quantum yield were up to 83% and 0.62 in turn. • Complex dynamic encryption and 3D modeling were achieved. Polymethyl methacrylate (PMMA) and polyvinyl alcohol (PVA) can provide a rigid and anaerobic microenvironment for pure organic room temperature phosphorescent (ORTP) luminogens, leading to significantly enhanced room temperature phosphorescence quantum yield (Φ P) and extended lifetime. However, their brittleness and hydrophilicity limit further expansion of the application range of ORTP luminogens. Herein, two novel ORTP luminogens named as PM-BTDA and OM-BTDA were designed and synthesized based on isomerization and intramolecular charge transfer effects. Interestingly, PM-BTDA showed longer RTP and afterglow (667.19 ms and 10 s) lifetimes, as well as higher Φ P (0.26) than OM-BTDA (527.17 ms, 8 s, and 0.21) in PVA film, but with long-lived visible afterglow for OM-BTDA rather than PM-BTDA in PMMA and polyvinyl chloride (PVC) films. More importantly, OM-BTDA@PVC film demonstrated an outstanding flexibility and mechanical force responsive behaviors, with continuously decreasing room temperature phosphorescent (RTP) intensity and lifetime within the elongation ranges from 0 to 147.49 %. Based on Foerster resonant energy transfer (FRET) from RTP of OM-BTDA/PM-BTDA to Rhodamine B (RhB) in PVA matrix, light yellow and red afterglows were achieved, with emission maxima of 587–612 nm, energy transfer efficiency of 83 %, and Φ P of 0.62. Based on different fluorescence and afterglow colors and lifetimes, as well as excellent flexibility of OM-BTDA@PVC film, complex dynamic digital encryption and various 3D models were successfully constructed. This work can be expected to provide more inspirations and possibilities for ORTP luminogens in molecular design and more application areas. [ABSTRACT FROM AUTHOR]

Commutative encryption and watermarking (CEW) is a seamless integration of encryption and watermarking technologies, overcoming the limitations of single security protection methods that may fail to provide comprehensive data protection. It is a research hotspot in the field of geographic information security. However, constructing or selecting stable features for vector point data to apply CEW algorithms remains a challenging problem. To address this, this paper utilizes space-filling curves to organize discrete points into line or polygon types and proposes a CEW algorithm for vector data based on prediction differences. Specifically, feature points are used to fit curves, providing independent operational spaces for watermarking and encryption. The segmented fitted curves are arbitrarily flipped to achieve perceptual encryption, while watermarks are embedded in the differences between the fitted curves and the actual data. Experimental results demonstrate that the proposed algorithm effectively addresses the issue of existing algorithms being unsuitable for point data. In simulated real-world scenarios, the extracted watermark information achieved NC values above 0.75, indicating strong robustness. Under conditions using existing lossless algorithms, the watermark capacity of the proposed method is thousands of times higher than that of existing algorithms, and it exhibits excellent encryption security, providing a new perspective for further research on lossless CEW. • The proposed algorithm solves the problem that cannot be applied to datasets. • Combination of reversible watermarking and encryption balances data accuracy and security. • This algorithm implements selective encryption, decryption and lossless recovery. • The algorithm is suitable for Geoinformation data with similar structure. [ABSTRACT FROM AUTHOR]

In Industrial Internet of Things (IIoT) production and operation processes, a substantial amount of video data is generated, often containing sensitive personal and commercial information. This paper proposed three new multiscroll Hopfield neural network (MHNN) systems by utilizing an improved segmented nonlinear non-ideal magnetic-controlled memristor model for electromagnetic radiation. Through dynamical methods, the constructed neural network's multidimensional multiscroll attractors and initial offset boosting behavior are analyzed. The observed initial offset boosting behavior demonstrates the system has extreme multistability. Secondly, a video encryption application based on the MHNN system is implemented on the Raspberry Pi platform. This approach encrypts each frame of the extracted video image using a novel encryption algorithm through frame-by-frame encryption, achieving significant encryption results with an information entropy calculation result of 7.9973. This provides strong protection for video data generated in IIoT. Finally, the proposed MHNN system is implemented on Field-Programmable Gate Array (FPGA) digital hardware platform. • A simple multi-stage magneto-controlled memristor is proposed. • HNN dynamic behaviors of multiscroll and extreme multistability are analyzed. • An efficient video data encryption scheme based on the MHNN system is introduced. • The feasibility of the MHNN system is confirmed through FPGA implementation. [ABSTRACT FROM AUTHOR]

The emergence of stimuli-responsive fluorescence anti-counterfeiting technology has garnered increasing attention in the era of intelligent internet. Smart fluorescent hydrogels combine the characteristics of luminous materials with the unique structure of hydrogels, offering the potential for dynamic reversible erasing and multi-tiered data encryption. In this work, a fluorescent hydrogel was constructed by zeolitic imidazolate framework-8 loaded with fluorescein and then mixed with polyvinyl alcohol hydrogel, sodium carboxymethyl cellulose and borax, which could be used for image hiding in visible light. The reversible bonds cross-linked fluorescent hydrogel was stretchable and self-healing with a three-dimensional network structure. The hydrogel presented bright green fluorescence under 365 nm UV light, which was quenched by adding copper ions. Meanwhile, the imprint of the hydrogel could be cleared by L-Cysteine and repeatedly recorded information many times. The alkali-induced shape memory capability was further utilized to achieve multi-tiered data encryption by deforming it to a 3D-specific shape through folding. The rewritable and multi-dimensional encrypted hydrogel is expected to improve data security and reduce resource consumption. The synthesis, storage of information and the prevention of counterfeiting of PVA/B/CMC/ZIF-8@Flu hydrogel. [Display omitted] • Fluorescein-encapsulated ZIF-8 and a fluorescent hydrogel were synthesized by a simple one-pot method. • The hydrogel is multi-functional with stretchability, self-repairing, antibacterial activity and water resistance. • The hydrogel is capable of storing information through Cu2+ quenching the fluorescence for anti-counterfeiting. • L-Cysteine restores the fluorescence of this hydrogel, enabling erasable information storage. [ABSTRACT FROM AUTHOR]

The Permuted Kernel Problem (PKP) is a problem in linear algebra that was first introduced by Shamir in 1989. Roughly speaking, given an ℓ × m matrix A and an m × 1 vector b over a finite field of q elements F q , the PKP asks to find an m × m permutation matrix π such that π b belongs to the kernel of A . In recent years, several post-quantum digital signature schemes whose security can be provably reduced to the hardness of solving random instances of the PKP have been proposed. In this regard, it is important to know the expected number of solutions to a random instance of the PKP in terms of the parameters q , ℓ , m. Previous works have heuristically estimated the expected number of solutions to be m ! / q ℓ. We provide, and rigorously prove, exact formulas for the expected number of solutions to a random instance of the PKP and the related Inhomogeneous Permuted Kernel Problem (IPKP), considering two natural ways of generating random instances. [ABSTRACT FROM AUTHOR]

• To enhance data security and privacy, the proposed SDRG-Net integrates MLCT. • To address class imbalance effectively, the system employs MOSD. • The DRCS-GCN is utilized to extract disease-specific features. • To achieve accurate grading of DR, the GDWO-EGB classifier is applied to refined features. Diabetic Retinopathy (DR) affects approximately 463 million individuals globally, with predictions indicating a rise to 700 million by 2045. Despite advancements, traditional DR detection methods face significant challenges, including limited diagnostic accuracy and the labour-intensive nature of manual image analysis. This work proposes the Secure DR Grading Network (SDRG-Net), which integrates Multi-Level Color Transformation (MLCT) for image encryption to ensure data security and privacy in Internet of Medical Things (IoMT) applications. The SDRG-Net employs the Minority Oversampling by Synthetic Data (MOSD) to address class imbalance, enhancing data quality and model robustness. The DR Class Specific-Graph Correlation Network (DRCS-GCN) is utilized for disease-specific feature extraction, effectively identifying and leveraging complex relationships within the data. The Gradient Decision Weights Optimization based Extreme Gradient Boosting (GDWO-EGB) classifier is then applied to these refined features for accurate DR grading. The performance of SDRG-Net is evaluated using the EyePACS and Messidor datasets, demonstrating significant improvements in classification accuracy and reliability compared to existing methods. [ABSTRACT FROM AUTHOR]

• A roadmap to fault diagnosis from from scratch to future perspectives. • Machine learning theories in fault diagnosis is systematically reviewed. • Deep learning focuses on further enhanced benefits in the present. • The future of deep learning theories i.e. transfer learning has been reviewed. In fault diagnosis, machine learning theories are gaining popularity as they proved to be an efficient tool that not only reduces human effort but also identifies the health conditions of the machines automatically. In this work, an attempt has been made to systematically review the progress of machine learning theories in fault diagnosis from scratch to future perspectives. Initially, artificial intelligence came into the picture which started to weaken the human effort whose efficiency relies on feature extraction which depends on expert knowledge. The introduction of deep learning theories has reformed the fault diagnosis process by realising the artificial aid, encouraging end-to-end encryption in the diagnostic procedure. The deep learning theories have also filled the gap between the large amount of monitoring data and the health conditions of industrial machines. The future of deep learning theories i.e. transfer learning which uses the knowledge of one domain to another related domain during fault diagnosis has been reviewed. In last, the research trends of the machine learning theories have been briefly discussed along with their challenges in fault diagnostics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Host and guest materials were easily prepared on a large scale. • The new doping system showed long-lived TADF and RTP lifetimes. • The persistent TADF, unimolecular and aggregated RTP were simultaneously found. • The doping systems presents temperature responsive dynamic afterglow. • High-level data encryption and dynamic anti-counterfeiting pattern were constructed. Designing persistent dual-band afterglow materials with thermally activated delayed fluorescence (TADF) and room temperature phosphorescence (RTP) contributed to solving the problems of homogenization and singularity in long afterglow materials. Here, six aryl acetonitrile (CBM) and aryl dicyanoaniline (AMBT) derivatives, used as host and guest materials respectively, were successfully designed and synthesized based on the isomerization effect. Among of them, 0.1 % m-CBM/p-AMBT showed the longest dual-band TADF (540 ms) and RTP lifetimes (721 ms), as well as persistent afterglow over 8 s, whose fluorescence (Φ FL), TADF (Φ T) and RTP (Φ P) quantum yields were 0.11, 0.06 and 0.22 in sequence. More interestingly, some doping systems constructed by CBM and AMBT series compounds showed persistent triple-band emissions composed of TADF, unimolecular and aggregated AMBT series compounds. What's more, Φ FL , Φ T and Φ P of 1 % o-AMBT@PMMA film were up to 0.17, 0.17, 0.23 in turn, with TADF, RTP and afterglow lifetimes of 606 ms, 727 ms, and 10 s respectively. TADF and RTP emission of CBM/AMBT series doping systems was attributed to host sensitized guest emission. Besides, the comparison displayed AMBT series compounds had bigger intensity ratios between TADF and RTP emission in PMMA films compared to in CBM series compounds. Finally, a series of data encryption were successfully constructed based on different afterglow lifetimes of the doping systems, and a dynamic anti-counterfeiting pattern was prepared by using different temperature responses of TADF and RTP emissions. [ABSTRACT FROM AUTHOR]