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Data breaches have the potential to weaken employee morale, corporate reputations, and customer and supplier relationships, while also disrupting marketing investments and financial performance. Research on reducing their frequency and harm focuses on tactical solutions, though breaches represent serious, even existential threats to firms. To date, research has not attempted to simultaneously address the closely connected phenomena of preventing and recovering from data breaches. The authors propose that corporate social responsibility (CSR) is a strategic variable offering dual protection: reducing the likelihood of data breaches and attenuating harm when breaches occur. Drawing on stakeholder theory, the authors distinguish between internal (addressing primary stakeholders) and external (addressing secondary stakeholders) CSR. Study 1 shows that external CSR has no prophylactic effect, while moderate and high levels of internal CSR are equally effective at preventing data breaches, compared with low levels of internal CSR. Study 2 assesses mitigation following a data breach by examining (1) short-term effects (in the form of an event study on cumulative abnormal returns) and (2) long-term effects (with time-series analysis of Tobin's q). The results suggest that internal CSR props up financial performance only at high levels while the positive effect of external CSR is short-lived. [ABSTRACT FROM AUTHOR]

This study discusses the importance of the DSpace open-source software that supports numerous digital libraries and repositories around the world. With the release of DSpace version 7, a natural question that arises is whether the new version offers enough new functionalities to motivate system administrators to upgrade. This paper briefly describes the most important changes, including new features and bug fixes, included in DSpace 7.4 and prior minor versions. The next parts of this paper explore our estimate that there are several thousand DSpace-based systems globally that will likely have to be upgraded in the near future. The main reason for this need is that older versions of DSpace (including 5.x) have reached the end of their developer support period or are reaching it in mid-2023. Based on our own upgrade experience, we propose suggestions and recommendations on migrating from the previous DSpace 6.3-based environment to the new one in a case study that concludes this article. [ABSTRACT FROM AUTHOR]

With the advancement of technology, Multimedia Social Networks (MSN) have become one of the most promising platforms for managing and sharing multimedia content. The user outsources sensitive content to the Multimedia Social Networks Service Provider (MSNSP). The MSNSP accumulates data in the network, and the user can process and share content like files, audios, and videos with other users through MSN such as YouTube, Vimeo, Flicker, Google Video, Cloud Drive, and so on. Despite receiving wide attention, two main drawbacks regarding MSN platforms are user privacy and data security. Ciphertext-policy attribute-based encryption (CP-ABE) is an appealing cryptographic primitive that plays the principal role for fine-grained access control on encrypted content, thus resolving security and privacy issues smoothly. Recently, there has been a trend to design ABE schemes incorporating a variety of additional properties. In this paper, we have assimilated revocability and key-homomorphic property in ciphertext-policy attribute-based key encapsulation mechanism (CP-ABKEM). Our designs are the first to achieve these additional two properties concurrently in an attribute-based setting. We have provided two instantiations of key-homomorphic revocableCP-ABKEM - one is selective secure against chosen-plaintext attack (CPA) in the standard model under the hardness of the q-decisional bilinear Diffie-Hellman exponent (q-DBDHE) problem, while the other achieves selective CPA security in the random oracle model under the hardness of the n-multilinear decisional Diffie-Hellman (n-MDDH) problem. Furthermore, our first scheme performs better than existing similar schemes in terms of communication overhead and master secret key size. As an advanced version of CP-ABKEM, our proposal is significant and may be utilized for various privacy-preserving protocols. [ABSTRACT FROM AUTHOR]

E-health has emerged as a key research area as long as the advent of the Internet of Things (IoT). Maintaining patients' privacy seems complex because patient data is so sensitive. In medical uses, patient health information is often kept in the cloud, which bounds the user's ability to fully control their data. To overcome the issues of privacy and security, medical data is collected from IoT sensors embedded in patients to the Personal Digital Assistant (PDA) for further processing. A hybrid encryption algorithm is used to ensure data security during transmission from the gathered medical-related data. The encrypted report is deposited in the cloud for later retrieval with appropriate access controls and encryption mechanisms in place. The use of blockchain for transmitting encrypted data further enhances the transmission of data securely and reduces the risk of data breaches. The generation of encryption and decryption keys using a hybrid deep learning model (LSTM and CNN) ensures the uniqueness and robustness of the keys. The selection of the optimal key using the Self-Improved Lion Optimization Algorithm (SI-LA) ensures the efficiency and effectiveness of the encryption and decryption process. Moreover, the execution of the model is equated with the existing technology; therefore, the proposed model is ensured as a more effective technique than the existing technique in terms of performance metrics. [ABSTRACT FROM AUTHOR]

The Cenozoic era is the digital age where people, things, and any device with network capabilities can communicate with each other, and the Internet of Things (IoT) paves the way for it. Almost all domains are adopting IoT from smart home appliances, smart healthcare, smart transportation, Industrial IoT and many others. As the adoption of IoT increases, the accretion of data also grows. Furthermore, digital transformations have led to more security vulnerabilities, resulting in data breaches and cyber-attacks. One of the most prominent issues in smart environments is a delay in data processing while all IoT smart environments store their data in the cloud and retrieve them for every transaction. With increased data accumulations on the cloud, most smart applications face unprecedented delays. Thus, data security and low-latency response time are mandatory to deploy a robust IoT-based smart environment. Blockchain, a decentralized and immutable distributed ledger technology, is an essential candidate for ensuring secured data transactions, but it has a variety of challenges in accommodating resource-constrained IoT devices. Edge computing brings data storage and computation closer to the network’s edge and can be integrated with blockchain for low-latency response time in data processing. Integrating blockchain with edge computing will ensure faster and more secure data transactions, thus reducing the computational and communicational overhead concerning resource allocation, data transaction and decision-making. This paper discusses the seamless integration of blockchain and edge computing in IoT environments, various use cases, notable blockchain-enabled edge computing architectures in the literature, secured data transaction frameworks, opportunities, research challenges, and future directions. [ABSTRACT FROM AUTHOR]

In federated learning, secret sharing is a key technology to maintain the privacy of participants' local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions. [ABSTRACT FROM AUTHOR]

In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), pneumonia kills about 2 million children under the age of 5 every year. Traditional machine learning methods can be used to diagnose chest X-rays of pneumonia in children, but there is a privacy and security issue in centralizing the data for training. Federated learning prevents data privacy leakage by sharing only the model and not the data, and it has a wide range of application in the medical field. We use federated learning method for classification, which effectively protects data security. And for the data heterogeneity phenomenon existing in the actual scenario, which will seriously affect the classification effect, we propose a method based on two-end control variables. Specifically, based on the classical federated learning FedAvg algorithm, we modify the loss function on the client side by adding a regular term or a penalty term, and add momentum after the average aggregation on the server side. The federated learning approach prevents the data privacy leakage problem compared to the traditional machine learning approach. In order to solve the problem of low classification accuracy due to data heterogeneity, our proposed method based on two-end control variables achieves an average improvement of 2% and an accuracy of 98% on average, and 99% individually, compared to the previous federated learning algorithms and the latest diffusion model-based method. The classification results and methodology of this study can be utilized by clinicians worldwide to improve the overall detection of pediatric pneumonia. [ABSTRACT FROM AUTHOR]

ABSTRACT As a fundamental component that supports business connectivity and enables intelligent decision‐making, vector spatial data encounter increasing copyright and security challenges throughout their lifecycle. There is an imminent need for a comprehensive lifecycle protection solution to ensure data security and adherence to copyright laws. This study introduces an integrated protection scheme designed to address copyright and security issues across the entire lifecycle of vector spatial data. First, DNA encryption is applied to the vector spatial data, with the key subsequently encrypted using the RSA public key. Concurrently, GD‐PBIBD fingerprint encoding is used to generate a fingerprint sequence. Thereafter, this sequence is scrambled using logistic mapping, and a quantization mechanism embeds the scrambled fingerprint sequence into the coordinates. Subsequently, the RSA private key is used to decrypt the key and extract the fingerprint information. Finally, logistic mapping is employed to restore the fingerprint sequence, and Hamming distance calculations are used to identify colluding users and trace potential data leaks. Experimental studies indicate that the proposed DNA encryption algorithm is effective for various types of vector spatial data, including point, polyline, and polygon data. It features a large key space and high key sensitivity. Furthermore, the GD‐PBIBD fingerprint encoding is simple to construct, offers excellent imperceptibility, and provides robust resistance to both single‐ and multi‐user attacks. This research addresses the gap in full lifecycle copyright and security protection for vector spatial data and offers a holistic solution for secure data circulation, thereby demonstrating significant practical value. [ABSTRACT FROM AUTHOR]

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images. [ABSTRACT FROM AUTHOR]

This paper examines the current status of China's legislation on safeguarding sovereignty and security, resource security, navigation security, ecological security, and regulating law enforcement and judicial activities, and summarizes the characteristics of existing marine security legislation. This paper utilizes literature analysis and comparative analysis methods, under China's Overall National Security Outlook, this paper proposes a path of improvement by consolidating marine-related norms, promoting the formulation of a basic law on the seas, refining the legal provisions on marine security, improving the marine data classification and grading system and safeguarding marine data security, and pushing forward the reform of the adjudication mechanism of the maritime courts to enhance the ability to actively safeguard the rights and interests of the oceans and seas through the use of the rule of law. [ABSTRACT FROM AUTHOR]

The use of digital technologies in qualitative research has been found to increase access and participation by minimizing geographical, scheduling, and financial barriers. However, discussions among the qualitative research community about the challenges of conducting research online and, specifically, what steps can be taken to mitigate "imposter participants" remain limited. Anchored in a critical disability studies perspective, in this field note paper, we discuss lessons learned in conducting online qualitative research and preserving data trustworthiness. [ABSTRACT FROM AUTHOR]

The demand for alternative methods of providing informed consent is increasing, especially in research with marginalised (or illiterate) research participants. This article discusses the co-creation of a visual informed consent (VIC), in collaboration with an artist. The VIC was inspired by the experience of obtaining informed consent from a group of migrant women with limited English proficiency, in empirical research undertaken on agroecology and health in Coventry, UK. Reflecting further on its creation and wider utility, this article explores the inner values that might guide researchers and lead to the co-creation of care-full tools that meet the needs of research participants. Specifically, this includes, reflecting on the iterative process of developing a VIC and using an ethics of care as a primary conceptual framework. Findings reveal that participants' understanding of ethical issues is facilitated using visual illustrations. It is argued that the creation of a VIC requires the researcher to be attentive to the embodied nature of research practice and guided by an ethics of care. A conceptual framework that integrates care and embodiment is presented, with the intention that it may further support the development of care-full research by others. [ABSTRACT FROM AUTHOR]

Current clinical research lacks diversity in those that participate. This lack of diversity is concerning given its importance for successful drug development. The frequency and severity of many diseases, along with the pharmacological properties of therapies, can display significant differences based on patient diversity. A clinical trial population that is more reflective of these differences will help researchers better understand the therapeutic profile of the treatment and provide generalizable knowledge to the medical community. The advent of decentralized clinical trial designs is meant to help address this lack of diversity by using portable digital health technologies and virtual interactions to enhance clinical trial access and broaden participation. By leveraging these technologies, trial conduct can occur at locations other than traditional research sites. This shift in trial location may help address some of the logistical, educational, engagement, and trust barriers that have historically prevented enrollment of diverse populations. However, these types of trials still have limitations. Ethical concerns around justice, equity, and diversity will still exist with decentralized clinical trials, which could be mediated using clinical research vehicles. When utilized, this modality may enhance the scientific design and conduct of clinical trials and better follow these ethical principles. These enhancements and improved ethical direction could be accomplished through increasing community involvement, improving health literacy, supporting more diverse trial sites, creating community‐based research footholds, fostering connections with researchers, limiting technical challenges, and preventing data security issues. [ABSTRACT FROM AUTHOR]

Objective: To identify differences in inbox and secure message burden among otolaryngologists based on demographics and subspecialty over 4 years. Methods: Inbox data were queried from January 2019 until December 2022. Otolaryngologists were categorized into cohorts by area of practice and gender. All inbox tasks, secure messages, and clinical encounters were collected and compared by gender, practice type, and years in practice. Means were compared using t-tests and chi-squared tests. Results: Of the 128 physicians, 45.7% were comprehensive otolaryngologists and 61.3% were male. The most common subspecialties were facial plastics (15.6%), oncology (8.6%), and otology (7.8%). Otolaryngologists had an average of 143.5 inbox tasks per month, with 97.2 (67.7%) of them being secure messages, resulting in an average of 1.14 inbox tasks and 0.80 secure messages per clinical encounter. The ratio of secure messages per clinical encounter was consistent across all specialties except oncology (1.10, P =.003). Otology (0.86, P =.032) and facial plastics (0.95, P =.028) had significantly lower ratios of inbox tasks to clinical encounters when compared to their colleagues, while oncology had a higher ratio (1.70, P <.001). No significant differences in inbox burden were observed between genders, years in practice, or languages spoken. Secure messages steadily increased over the study period. Conclusion: Inbox burden for otolaryngologists primarily stems from patient secure messages and varies across subspecialties. Considerations should be made to the inbox burden of head and neck oncologists. The implementation of support systems for inbox management could improve the imbalance between clinical and non-clinical responsibilities in otolaryngology. Level of Evidence: Level III, Retrospective Cohort Study [ABSTRACT FROM AUTHOR]

This paper examines the integration of artificial intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process make AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas such as cardiology and dermatology. Focusing on radiotherapy, the paper details AI's applications in target segmentation, dose optimization, and outcome prediction. It discusses adaptive radiotherapy's benefits and AI's potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI's role is crucial as it acknowledges potential job-role changes and concerns about patients' trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI's potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilization. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its' success in radiotherapy and healthcare. [ABSTRACT FROM AUTHOR]

This paper explores the potential of metaverse technology for the preservation of cultural heritage while acknowledging the obstacles that come with its implementation. It begins by highlighting the importance of safeguarding cultural heritage amidst the backdrop of globalization and technological advancements. The emergence of immersive technologies which include Virtual Reality, Augmented Reality and the Metaverse, is then introduced as a promising solution to address this challenge. The paper emphasises the transformative power of the metaverse in revitalising our interactions with digital content. Nevertheless, it also identifies various challenges associated with employing metaverse technology for cultural preservation. These challenges encompass technical limitations, ethical considerations regarding cultural representation and ownership, as well as concerns about privacy, data security, and digital rights management. Despite these hurdles, the research advocates for the utilization of immersive technology to protect and celebrate cultural heritage. It underscores the necessity for collaboration among stakeholders, such as technology developers, cultural institutions, communities, and policymakers, to tackle these challenges and ensure responsible and ethical preservation practices. [ABSTRACT FROM AUTHOR]

This study tests the person–environment fit theory in information security (ISec), which describes situations in which the extent of ISec management is linked to positive or negative employee outcomes. We hypothesise that employees appraise their organisational ISec depending on the mismatch or match perception between their needs and organisational supplies. We define a mismatch as ISec misfit and a match as ISec fit and develop hypotheses framing the relationship between ISec fit (vs. misfit), employees' emotional reactions (anxiety and attentiveness), and, subsequently, ISec behaviours (ISec in-role, extra-role, and violation behaviours). We conduct quadratic polynomial regressions and apply response surface methodology to test our hypotheses and obtain two findings. First, ISec management are proactively needed by employees instead of prior enforcement perspective. The results show that ISec management practices are appraised negatively when they either exceed or fail to meet employees' preferences. Second, in expounding on a theoretical construction and empirical test of ISec fit and misfit and their relationships with employee outcomes, we find that attentiveness is greater with ISec fit, whereas anxiety is higher and attentiveness is lesser with ISec misfit. We also find that emotion mediates employees' ISec behaviours, while only anxiety does not mediate extra-role behaviour. [ABSTRACT FROM AUTHOR]

Security and privacy have become major issues. Although no one seriously claims that privacy and security are identical, most sources treat them as if they are. Differentiating between privacy and security is essential, for example, in those situations where there is a trade-off between the two concepts, or when a regulator imposes rules concerning privacy or security or both. Moreover, differentiation can support the selection of proper means of defence. Therefore, there is a gap between the need to differentiate and the availability of an appropriate methodology to do so. This article introduces a robust methodology for differentiating between privacy and security, based on four basic components: the target of the attack, the nature of the cost, the presence of a trade-off, and the existence of consent. Since many situations involve both privacy and security, the methodology ranks their level independently. The research addresses several goals: alignment with the prevalent definitions and concepts in the literature; achieving objectivity; and a non-dichotomous classification. The application of this methodology was demonstrated in an empirical study with $n = 155$ n = 155 valid participants. The empirical study indicated significant differences between intuitive classification and methodology-based classification, thereby emphasising the need for this methodology. [ABSTRACT FROM AUTHOR]

In recent years, information security has gained significant academic attention. The present research sought to examine shifts in undergraduates' Information Security Awareness (ISA) as they advanced in their academic pursuits. Comprising two distinct phases, the initial phase involved adapting the Human Aspects of Information Security Questionnaire (HAIS-Q) to fit the context of Chinese tertiary institutions, a process overseen by five eminent ISA specialists. The adapted instrument was then piloted with 647 students, utilising snowball and criterion sampling techniques. Construct validity was established through exploratory factor analysis, and the questionnaire's reliability was ascertained via Cronbach's alpha. The subsequent primary survey engaged 5148 undergraduates from three Chinese academic institutions to further validate the instrument and investigate its relevance to student cohorts. Distinctively, participants exhibited pronounced proficiency in the Attitude component, yet lagged in Behavioural aspects. Analytical outcomes highlighted discernible interactions between gender, academic progression, and the Knowledge, Attitude, and Behaviour (KAB) facets. Notably, female respondents consistently outperformed their male counterparts. However, a marked decline in ISA cognisance and comportment was observed as students ascended academically. The HAIS-Q stands as a reliable tool for evaluating ISA in tertiary education, offering fresh insights to the scholarly discourse on educational information security. [ABSTRACT FROM AUTHOR]

The digitalisation of healthcare has increased the risk of cyberattacks in this sector, targeting sensitive personal information. In this paper, we conduct a systematic review of existing solutions for data breach mitigation in healthcare, analysing 99 research papers. There is a growing trend in research emphasising the security of electronic health records, data storage, access control, and personal health records. The analysis identified the adoption of advanced technologies, including Blockchain and Artificial Intelligence, alongside encryption in developing resilient solutions. These technologies lay the foundations for addressing the prevailing cybersecurity threats, with a particular focus on hacking or malicious attacks, followed by unauthorised access. The research highlights the development of strategies to mitigate data breaches and stresses the importance of technological progress in strengthening data security. The paper outlines future directions, highlighting the need for continuous technological progress and identifying the gaps in the attack mitigations. [ABSTRACT FROM AUTHOR]

Summary: The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of cloud storage, deduplication technology has been widely used. In personal cloud storage, files are diverse. Diverse files contain files of different sizes and different popularity, some of which have many copies. Existing deduplication schemes mainly focus on data security but fail to improve the overall performance, including low computing overhead for diverse data. Using deterministic tags based on convergent encryption to identify data may leak data information, while using fully random tags generated by complex encryption algorithms may generate more computing overhead. To address the above issues, we proposed an encrypted deduplication scheme based on files diversity (FD-Dedup). Diverse files are identified by semi-random tags. We also designed a semi-random tag generation (SRTG) algorithm that coordinates the computing overhead and security. Security analysis and performance comparison show that FD-Dedup can balance security and computing overhead for diverse files in personal cloud storage. [ABSTRACT FROM AUTHOR]

In the digital information age, distributed file storage technologies like the InterPlanetary File System (IPFS) have gained considerable traction as a means of storing and disseminating media content. Despite the advantages of decentralized storage, the proliferation of decentralized technologies has highlighted the need to address the issue of file ownership. The aim of this paper is to address the critical issues of source verification and digital copyright protection for IPFS image files. To this end, an innovative approach is proposed that integrates blockchain, digital signature, and blind watermarking. Blockchain technology functions as a decentralized and tamper-resistant ledger, recording and verifying the source information of files, thereby establishing credible evidence of file origin. A digital signature serves to authenticate the identity and integrity of the individual responsible for uploading the file, ensuring data security. Furthermore, blind watermarking is employed to embed invisible information within images, thereby safeguarding digital copyrights and enabling file traceability. To further optimize the efficiency of file retrieval within IPFS, a dual-layer Distributed Hash Table (DHT) indexing structure is proposed. This structure divides file index information into a global index layer and a local index layer, significantly reducing retrieval time and network overhead. The feasibility of the proposed approach is demonstrated through practical examples, providing an effective solution to the copyright protection issues associated with IPFS image files. [ABSTRACT FROM AUTHOR]

To solve the problems of unstable chain and poor supply chain (SC) function in the China’s agriculture industry, the relevant study is carried out on the function evaluation of the sustainable agricultural SC by structural equation modelling (SEM) under the background of digital technology. Primarily, the analysis is implemented on the research status of the sustainable SC and SC function. Then, starting with the relationship among big data (BD) application, information sharing, and sustainable SC function of agricultural production, a function evaluation model is implemented for the sustainable SC of SEMbased agricultural production. Next, a questionnaire is designed for the research on the impact of BD applications on the function of the sustainable agricultural SC. Ultimately, the statistical analysis of the obtained data reveals the correlation between the interaction and influence among the BD application, information sharing, and sustainable SC function of agricultural production. The results show that the BD application dimension, information sharing dimension, and all sub-dimensions have is beneficial to the function of the agricultural sustainable SC. The application of multidimensional cloud technology in BD has no obvious relationship with the dimensionality and level of information sharing, and the relationship between other corresponding dimensions shows a significant positive impact. A referenceable method is provided for the practice of sustainable SC management of agricultural production. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, rural financial management is facing the great challenge of digitalization and intelligentization. This study aims to explore the application of embedded systems combined with blockchain technology in rural financial management to provide more efficient and secure solutions. Firstly, the main problems in rural financial management are analyzed, including low data processing efficiency and poor data security. It then explores the potential applications of embedded systems in financial management, as well as the main characteristics and classifications of blockchain technology. Based on this, a model integrating embedded system and blockchain technology is designed, and the effectiveness of the model is verified by experiments. The experimental results show that the system not only significantly improves the data processing speed, but also enhances the data security and improves the user satisfaction. In general, this study provides theoretical and practical support for the technical innovation of rural financial management, and shows a broad prospect in practical application. [ABSTRACT FROM AUTHOR]

The elliptic curve cryptosystem (ECC) has several applications in Information Security, especially in cryptography with two main activities including encrypting and decrypting. There were several solutions of different research teams which propose various forms of the elliptic curve cryptosystem on cryptographic sector. In the paper, we proposed a solution for applying the elliptic curve on cryptography which is based on these proposals as well as basic idea about the elliptic curve cryptosystem. We also make comparison between our proposal and other listed solution in the same application of the elliptic curve for designing encryption and decryption algorithms. The comparison results are based on parameters such as time consumption (t), RAM consumption (MB), source code size (Bytes), and computational complexity. [ABSTRACT FROM AUTHOR]

In the dynamic domain of healthcare data management, safeguarding sensitive information while ensuring data efficiency is always of the highest priority. Healthcare data are frequently mishandled, posing significant risks. This research offers a new network that assesses the quality of visual data using robust features‐driven Mutual Entropy Gain (MEG). The proposed network addresses a critical gap in healthcare data management, significantly enhancing patient data security and operational efficiency in medical institutions. Our method begins with a thorough empirical investigation to find the optimal intermediate features for network input. We incorporate both distance entropy and probability entropy adopted and normalized in MEG, resulting in a comprehensive healthcare data quality evaluation. The results show that the network can distinguish between high‐quality and low‐quality data based on information content. Furthermore, our assessment reveals a large performance discrepancy between high and low‐quality data, even with variable datasets. Notably, using only half of the data achieves commendable accuracy when compared with using the complete dataset, demonstrating possible efficiency gains. This breakthrough has far‐reaching implications for healthcare providers, potentially reducing data storage costs, accelerating data processing times, and minimizing the risk of data breaches. In essence, our proposed network enhances efficiency and security in healthcare data and adapts to the evolving landscape of convergence ICT, paving the way for more robust, cost‐effective, and secure healthcare information systems that can significantly improve patient care and operational outcomes. [ABSTRACT FROM AUTHOR]

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting. [ABSTRACT FROM AUTHOR]

Summary: The exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security. [ABSTRACT FROM AUTHOR]

Human healthcare is the most fundamental need for life and growth, and as such, it will forever be a topic of intense interest in human civilization. A nation's society cannot grow sustainably; much alone address other issues, if its population is not in good health. Thus, all civilizations and individuals have always placed a high priority on health. As a result, maintaining a healthy lifestyle requires physical fitness. Fitness guidance platforms that could offer individualized and data-driven scientific guidance are needed due to the development of technology and the rising interest in health and fitness. A scientific fitness guidance model is a method or framework that makes use of empirical research, scientific concepts, and modern technology to provide individualized guidance and suggestions for people's fitness and health-related objectives. As a result, we conduct research on developing an Internet of Things (IoT)-based scientific fitness guidance model. The IoT technology, in particular, is used as the paper's guiding principle to completely address the variety of fitness requirements of fitness viewers. Utilizing performance indicators, including accuracy, user experience, reliability, error rate, health outcomes, data security, and energy efficiency the suggested IoT paradigm can be evaluated. The findings demonstrate that the incorporation of IoT into the development of scientific fitness leads to enhanced efficiency for people. [ABSTRACT FROM AUTHOR]

Rationale, Aims, and Objectives Method Results Conclusion Medical device‐integrated electronic medical records (MDI‐EMR) pose significant challenges in ensuring effective usage, data security and patient safety. The complexities of MDI‐EMR necessitate applying various security mechanisms to safeguard against cyber threats. Therefore, we evaluated cyber threats to MDI‐EMR and the effectiveness of applied security controls using a proposed framework from sociotechnical and risk assessment perspectives.We conducted a qualitative case study evaluation in a general hospital in Saudi Arabia using interviews, observation, and document analysis from the perspectives of major MDI‐EMR stakeholders, including healthcare providers, IT professionals and cybersecurity specialists.The results showed the interplay among physical, technical and administrative security controls that maintained a secure posture of MDI‐EMR. The effectiveness of security controls is highly influenced by the staff's cybersecurity awareness and training. The perceived effectiveness of security controls varied among users, with some expressing satisfaction with the ease of use and reliability, while others highlighting challenges such as password complexity and access procedures. Understanding these diverse perspectives is crucial for tailoring security measures to meet the needs of different stakeholders effectively.Collaboration among the key stakeholders is crucial for implementing security controls for MDI‐EMR. Balancing security measures with usability concerns is essential, as highlighted by challenges in implementing technical controls. A comprehensive approach encompassing physical, technical and administrative controls, continuous education and awareness initiatives are significant to empower staff in recognising and mitigating cyber threats effectively to safeguard medical data and ensure the integrity of healthcare systems. [ABSTRACT FROM AUTHOR]

Secure instant communication is an important topic of information security. A group chat is a highly convenient mode of instant communication. Increasingly, companies are adopting group chats as a daily office communication tool. However, a large volume of messages in group chat communication can lead to message overload, causing group members to miss important information. Additionally, the communication operator's server may engage in the unreliable behavior of stealing information from the group chat. To address these issues, this paper proposes an attribute-based end-to-end policy-controlled signcryption scheme, aimed at establishing a secure and user-friendly group chat communication mode. By using the linear secret sharing scheme (LSSS) with strong expressive power to construct the access structure in the signcryption technology, the sender can precisely control the recipients of the group chat information to avoid message overload. To minimize computational cost, a signcryption step with constant computational overhead is designed. Additionally, a message-sending mechanism combining "signcryption + encryption" is employed to prevent the operator server from maliciously stealing group chat information. Rigorous analysis shows that PCE-EtoE can resist adaptive chosen-ciphertext attacks under the standard model. Simulation results demonstrate that our theoretical derivation is correct, and that the PCE-EtoE scheme outperforms existing schemes in terms of computational cost, making it suitable for group chat communication. [ABSTRACT FROM AUTHOR]

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management—findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings. [ABSTRACT FROM AUTHOR]

The booming demand on data security has aroused great interest for developing smart materials with temporal display feature and dynamic multicolor fluorescence. However, it remains challenging to implement both features on most responsive molecules. Herein, we construct a polymer free volume‐controlled "molecular clock and emitter" via covalently embedding a multi‐stimuli responsive molecular switch (i.e. spiropyran) into a polymer network (i.e. poly(pentafluorophenyl acrylate)) with programmable crosslink density and free volume. By the aminolysis of pentafluorophenyl ester with different amount of diamine crosslinkers, pPFPA‐co‐SP networks with controllable crosslink densities are generated, which have different confinement effects on the rate constant of SP/MC isomerization, thus leading to time‐dependent photochromism. In addition, PTF1, a fluorescent probe that is sensitive to polymer rigidity, is introduced to further endow pPFPA‐co‐SP system with phototunable dynamic full‐color emission. Therefore, relying on their synergistical responses to the rigidity of the polymer network, we have successfully developed a versatile molecular clock and emitter via an "one stone two birds" manner, which shows time‐dependent data display along with dynamic multicolor fluorescence switching, providing great potential for advanced encryption and anticounterfeiting with a high security level. [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]

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids. [ABSTRACT FROM AUTHOR]

Recently, cloud computing has become a growing technology in the information technology industry because of its several smooth delivery services. In cloud computing, multi-tenancy is one of the primary features that affords economic and scalability significance to the service providers and end-users by distributing a similar cloud platform. Due to the increasing demand for cloud computing, cloud usage has increased, so various vulnerabilities and threats have also been enhanced. Hence, data security and privacy are considered the major issues of multi-tenant environments in the cloud. Several existing studies have developed different mechanisms to solve security issues in multi-tenant cloud environments. However, they faced various problems while improving security, and this led to a lack of confidentiality, authenticity, and data integrity. Thus, this research paper intends to propose an efficient encryption approach for securing data delivery in the cloud with reduced time. For secure data delivery, homomorphic encryption is utilized to encode the cloud server's data. In homomorphic encryption, four stages are available for data delivery: key generation, encryption, decryption, and evaluation. The main problems in this homomorphic encryption mechanism are key sharing and key management. Due to these problems, the performance of homomorphic encryption is diminished. Thus, the proposed work introduces an Aquila optimizer for the key generation process. In this, optimal keys are selected, and it provides improved data security and privacy for cloud users. Finally, the selected keys are generated for the encryption and decryption process. The efficiency of the proposed approach is proved by comparing the performance in terms of encryption time, decryption time and throughput over the existing schemes like Rivest, Shamir and Adleman, ElGamal, Algebra Homomorphic Encryption scheme based on ElGamal (AHEE) and modified AHEE. The experimental results reveal that the proposed model achieves reduced encryption and decryption time of 972 ms and 4261 ms for the data size ranges from 5 to 25 mb. [ABSTRACT FROM AUTHOR]

In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the "Google Health Studies" mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency. [ABSTRACT FROM AUTHOR]

Data auditing permits data owners to confirm the reliability of their personal information without obtaining it from an unsafe cloud. Advertising cloud storage has widely adopted this characteristic. Data integrity is more difficult to maintain, and data through the cloud is not always secure. The ability to access data remotely through cloud storage makes it necessary to protect it against unauthorized access, hackers, and other malicious activity. In order to overcome these concerns, a hybrid Advanced Encryption Standard- Elliptic Curve Cryptography (AES-ECC) algorithm and Merkle Hash (MHT) Tree is developed. The user data is sent to the cloud service provider via blockchain for storage purposes. The key generation and encryption are carried out using a hybrid AES-ECC encryption technique, which is used to generate both private and public keys. The encrypted user file is then divided into several blocks. The tags for the split encrypted blocks are then generated using SHA256, which provides the hash value of each smaller file. Cloud Service Provider (CSP) constructs an MHT by employing generated hashtags to return the proof to user information after establishing hashtags for encrypted file blocks in the blockchain storage. Merkle hash tree is frequently employed to validate data integrity. Furthermore, the third-party auditor determines if the proofs generated by CSP match one from the user to check the tamper. The simulation analysis shows that the accuracy, encryption, decryption, key generation, and execution time reached for the proposed approach is 96%, 190 s, 124 s, 5.9 s, and 440 s. The proposed algorithm effectively validates data integrity and provides improved data security in blockchain environment. [ABSTRACT FROM AUTHOR]

Using a German online panel, we investigate respondents’ propensity to consent to the linkage of publicly available Facebook data to their survey data and to enable linkage. We analyse the effects of three experimental variations on consent and linkage among Facebook users: (1) the consent question wording, (2) the position of the Facebook consent question in a series of consent questions, and (3) the incentive offered. To describe potential selection bias, we consider respondents’ attitudes and sociodemographic characteristics. We found effects of consent question placement on consent and linkage and effects of higher incentives on linkage, but we found no effects of question wording. Our analysis also showed that linkage was dependent on privacy and data security concerns, trust in the data-collecting actor, respondents’ attitudes toward surveys, and several sociodemographic characteristics. [ABSTRACT FROM AUTHOR]

Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness. This study presents MyThisYourThat (MyTH) approach, which adapts an interpretable prototypical part learning network to a distributed setting, enabling each client to visualize feature differences learned by others on their own image: comparing one client's 'This' with others' 'That'. Our setting demonstrates four clients collaboratively training two diagnostic classifiers on a benchmark X-ray dataset. Without data bias, the global model reaches 74.14% balanced accuracy for cardiomegaly and 74.08% for pleural effusion. We show that with systematic visual bias in one client, the performance of global models drops to near-random. We demonstrate how differences between local and global prototypes reveal biases and allow their visualization on each client's data without compromising privacy. [ABSTRACT FROM AUTHOR]

Background: The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration. Results: To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data. Conclusions: In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC. [ABSTRACT FROM AUTHOR]

This paper presents a study on the applicability of blockchain technology in an e-learning system. The technical characteristics and operational costs of using blockchain technology were examined and evaluated to select the appropriate technology for a specific e-learning platform. A comparative analysis of leading Level 1 (L1) blockchain platforms was conducted, focusing on four main metrics: average block generation time, transaction cost, transaction finalization time, and chain throughput measured in transactions per second for each chain. The objective is to assess and compare key parameters that are crucial when choosing a specific blockchain for recording information under real-world conditions in e-learning systems. Methods for analyzing publicly available information were applied, combined with verification of data compatibility and consistency from various sources. [ABSTRACT FROM AUTHOR]

Providing appropriate patient education during a medical encounter remains an important area for improvement across healthcare settings. Personalized resources can offer an impactful way to improve patient understanding and satisfaction during or after a healthcare visit. ChatGPT is a novel chatbot— computer program designed to simulate conversation with humans— that has the potential to assist with care-related questions, clarify discharge instructions, help triage medical problem urgency, and could potentially be used to improve patient-clinician communication. However, due to its training methodology, ChatGPT has inherent limitations, including technical restrictions, risk of misinformation, lack of input standardization, and privacy concerns. Medicolegal liability also remains an open question for physicians interacting with this technology. Nonetheless, careful utilization of ChatGPT in clinical medicine has the potential to supplement patient education in important ways. [ABSTRACT FROM AUTHOR]

Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of data security in these structures remains a gap in current research. Our study aims to bridge this gap by developing a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. The research progresses from the derivation of an exact formula for falsification probability to an approximation suitable for cases with significantly large hash lengths. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths. A numerical analysis quantifies the discrepancy between exact and approximate probabilities, underscoring the conditions for the effective application of the approximation. This work offers crucial insights into optimizing Merkle Tree structures for bolstering security in blockchain and IoT systems, achieving a balance between computational efficiency and data integrity. [ABSTRACT FROM AUTHOR]

Modern technologies like quantum and cloud computing have reshaped computing, offering immense power and scalability. While beneficial, they also challenge the security of traditional cryptographic systems. Quantum computing threatens RSA and ECC with algorithms like Shor's algorithm, which can accelerate computations exponentially. This risks exposing these systems to attacks, necessitating quantum-resistant cryptography. Cloud computing poses data security concerns, requiring robust cryptographic mechanisms and access controls. Lattice-based cryptography, leveraging problems like the Short Integer Solution (SIS), emerges as a solution. This paper presents a novel quantum-resistant public key encryption scheme based on ElGamal and SIS, ensuring security against quantum and classical threats in modern cryptographic environments. [ABSTRACT FROM AUTHOR]

With the increasing use of technology in nursing, how nurses perform practice care has changed, inevitably leading to ethical concerns that differ from original ethical norms in nursing. Studies have focused on ethical issues in health informatics from clinicians' or patients' perspectives, while nurses' perspective is needed. This paper conducts a theoretical study on ethical predicaments that arise in nursing informatics from nurses' perspectives. Why and how these predicaments emerge are elaborated. Also, this paper offers countermeasures in realistic contexts from technique, education, and leadership aspects. Collaborations between governments, administrators, educators, technicians, and nurses are needed to step out of these predicaments. [ABSTRACT FROM AUTHOR]

As the use of artificial intelligence (AI) technologies, particularly generative AI (Gen AI), becomes increasingly prevalent in nursing education, it is paramount to address the ethical implications of their implementation. This article explores the realm of cyberethics (a field of applied ethics that focuses on the ethical, legal, and social implications of cybertechnology), highlighting the ethical principles of autonomy, nonmaleficence, beneficence, justice, and explicability as a roadmap for facilitating AI integration into nursing education. Research findings suggest that ethical dilemmas that challenge these five principles can emerge within the context of nursing education; however, adherence to these very principles, which is essential to improving patient care, can offer solutions to these dilemmas. To ensure the ethical and responsible use of Gen AI in nursing education, these principles must be woven into the fabric of curricula, and appropriate guidelines must be developed. Nurse educators have a pivotal role in strategizing comprehensive approaches for ethical AI integration, establishing clear guidelines, and instilling critical thinking among students. Fostering lifelong learning and adaptability is key to ensuring that future nurses can successfully navigate the constantly evolving landscape of health care technology. Future research should investigate the long-term impacts of AI utilization on learning outcomes and ethical decision-making. [ABSTRACT FROM AUTHOR]

An introduction to articles published within the issue is presented including the financial impacts of service lines in rural hospitals, the role of artificial intelligence in medical care and the use of dynamic capabilities to improve performance improvement in hospitals.