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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]

In the 21st-century digital age, health data privacy remains a crucial concern. This paper evaluates the effectiveness of the Health Insurance Portability and Accountability Act, known as HIPAA. More specifically, it demonstrates a need for a unified federal framework in the U.S. that aligns with General Data Protection Regulation’s protections to address modern-day cybersecurity threats better. This article argues that in an era of increased globalization, the United States should confront the task of reforming its healthcare data protection law to align with current cybersecurity risks. We begin by examining landmark legislation across American states to reveal inconsistencies between state and federal protective rulings. Later, we uncover the reactive nature of HIPAA, in contrast to GDPR’s proactive and citizen-centric approach. Through evaluating past lawsuits related to patient protection noncompliance, this paper depicts significant differences in the purpose, coverage, and execution of data protection laws between the United States and the European Union. It highlights GDPR’s effectiveness in granting individuals greater control over their data. Furthermore, this article proposes the adoption of newfound systems for standardized risk analysis and enhanced security across healthcare providers.As healthcare becomes more accessible to the American public, the amount of data in this system increases. This nationwide surge in data underscores the critical need to assess whether privacy laws established in the 1990s remain sufficient. Therefore, updates to healthcare legislation are essential to establishing stringent patient protections in response to the significant rise in data breach incidents within the healthcare network.

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]

In today's digital world, where the amount of information that is stored and processed in cyberspace is constantly growing, the issue of personal data protection is becoming more and more relevant. Personal data, such as financial information, medical records, and social media data, is a valuable resource that can be used for both legitimate and malicious purposes. Despite the continuous improvement of security technologies, the growing complexity of cyber threats and the evolution of cyber attack methods create new challenges for the protection of personal information. In this connection, there is a need to analyze the modern problems of personal data protection, in particular the legal, technical and ethical aspects of this issue. Ensuring the security of personal data remains an urgent problem today, because more and more people use it. On the Internet, they pass authorization on various resources, using their personal data for this purpose, and more and more different institutions create peculiar databases, not always taking care of their safety. The insufficient measure of ensuring the security of personal data in cyberspace leads to excessive activity of cyberattacks and the commission of various manipulations with personal data. The relevance of the topic of personal data protection in cyberspace is due to several important factors that have a significant impact on modern society, economy and security. The main aspects that emphasize the relevance of this issue: the growth of digitalization; cyber threats; legal requirements; social responsibility and trust; economic consequences; technological challenges; international relations and geopolitics. Thus, the relevance of the topic of personal data protection in cyberspace is due to the complexity and global nature of this issue, as well as its importance for preserving privacy, security and trust in the digital world. This article is aimed at researching key challenges in the field of personal data protection in cyberspace, as well as providing recommendations for increasing the level of security and minimizing the risks of information leakage. [ABSTRACT FROM AUTHOR]

Summary: Blockchain (BC) technology has been incorporated into the infrastructure of different kinds of applications that require transparency, reliability, security, and traceability. However, the BC still has privacy issues because of the possibility of privacy leaks when using publicly accessible transaction information, even with the security features offered by BCs. Specifically, certain BCs are implementing security mechanisms to address data privacy to prevent privacy issues, facilitates attack‐resistant digital data sharing and storage platforms. Hence, this proposed review aims to give a comprehensive overview of BC technology, to shed light on security issues related to BC, and to emphasize the privacy requirements for existing applications. Many proposed BC applications in asset distribution, data security, the financial industry, the Internet of Things, the healthcare sector, and AI have been explored in this article. It presents necessary background knowledge about BC and privacy strategies for obtaining these security features as part of the evaluation. This survey is expected to assist readers in acquiring a complete understanding of BC security and privacy in terms of approaches, ideas, attributes, and systems. Subsequently, the review presents the findings of different BC works, illustrating several efforts that tackled privacy and security issues. Further, the review offers a positive strategy for the previously described integration of BC for security applications, emphasizing its possible significant gaps and potential future development to promote BC research in the future. [ABSTRACT FROM AUTHOR]

Pattern recognition of optical label is utilized not only in packet switches but also in the field of data security due to its wide range of applications. In particular, pattern recognition using waveguide devices is highly valued because it eliminates the need for encoder processing. However, existing methods suffer from the problem of degraded contrast ratio because half of the input power are emitted from non‐target waveguides. In this article, a method is proposed to improve the contrast ratio employing a 4×$\times$4 multi‐mode interference (MMI) coupler with multiple input signals. The theoretical results reveal that the proposed method improves the contrast ratio from 3.0 dB to 9.5 dB and ∞$\infty$ dB for the three‐input and four‐input cases, respectively, for quadrature phase‐shift keying (QPSK)‐modulated signal. Furthermore, numerical simulation was performed through the three dimension finite‐difference time‐domain (3D‐FDTD) method, and the proposed scheme successfully discriminated each QPSK pattern. The power value |E|2$|E|^2$ at all ports closely matches the theoretical value, and the ratios of 9.5 dB and >$>$30.0 dB are numerically obtained. [ABSTRACT FROM AUTHOR]

A self-organizing network with dispersed functionalities is known as a mobile-adhoc network (MANET). Modern technology has made integration, MANET, and the Internet of Things (IoT) very desirable. The MANET-IoT architecture that has been suggested uses a multi-cluster setup. Hybrid K-Mode Clustering (HKMC) is a method that has been suggested for use in cluster building. An approach called Spider Monkey Optimization (SMO) is used in each cluster to identify the best cluster head (CH). This algorithm takes into account numerous objective functions. An Energy-aware Multi-Attribute Trust (EMAT) model is proposed to ensure trusted environment. The EMAT algorithm works on twofold: (i) first, multi-attribute based trust value is computed by Multi-Agent Reinforcement Learning (MA-RL) algorithm and (ii) next, optimal route is selected by Secure & Energy Score (SES) based methodology. For data security, Multi-Attribute Cryptography (MAC) approach is presented. On the whole, the energy efficiency and security is improved for MANET-IoT environment. There is an improvement to the network in terms of residual energy, protection level, throughput, routing overhead, energy efficiency, and packet delivery ratio (PDR) according to the testing findings. [ABSTRACT FROM AUTHOR]

Precision agriculture, a transformative farming approach, has gained prominence due to advancements in digital technologies. This paper explores the multifaceted landscape of precision agriculture, focusing on its tangible benefits, challenges, and future directions. Purpose: Amidst the growing interest in precision agriculture, this paper aims to provide a comprehensive analysis of its various aspects. Specifically, it seeks to elucidate the benefits of precision agriculture in optimizing resource utilization, enhancing crop health, and promoting sustainability. Moreover, it examines the challenges faced in its implementation and proposes future directions to overcome these obstacles. Findings: Through a review of existing literature and case studies, this paper presents a nuanced understanding of precision agriculture's impact on crop farming, livestock production, economic outcomes, and environmental sustainability. It identifies key challenges such as data security, implementation costs, and regulatory frameworks, while also highlighting innovative solutions and promising advancements in the field. Originality: To the best of our knowledge, this paper represents a rigorous attempt to comprehensively analyze the landscape of precision agriculture, with a focus on its original contributions to the field. By synthesizing existing research and offering insights into future directions, it adds to the emerging knowledge base surrounding precision agriculture and its potential to revolutionize modern farming practices. [ABSTRACT FROM AUTHOR]

Cloud Computing is a new paradigm that provides software and hardware resources based on customers’ needs. However, data security remains a significant concern, acting as a major barrier to its widespread adoption. The objective of this work is to propose a process for assessing data risks in Cloud Computing. The process is based on fuzzy cognitive maps (FCMs) for qualitative reasoning. Risk assessment is a crucial aspect of Cloud Computing. Our proposed system involves three main steps based on expert knowledge. In the first step, a global FCM was built and validated to identify threats at the Cloud Computing level. Then the second step was devoted to calculate the consequences by inference using FCM Expert. The obtained results show that the FCM has a remarkable capacity to deal with the uncertainty of the data; by doing so, it becomes possible to model the real system more accurately. The last step is to simulate, analyze the results of the second step in Matlab Simulink and also develop recommendations to reduce the risks in order to improve and increase the security of Cloud data. [ABSTRACT FROM AUTHOR]

ABSTRACT The recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model. [ABSTRACT FROM AUTHOR]

To improve the data security and integrity of the outsourced data, storing multiple copies of data on multiple cloud servers is a good way. Many public Provable Data Possession (PDP) schemes in multiple cloud servers have been proposed in recent years. However, in some scenarios, the Data Owner (DO) may not want anyone (e.g. a stranger) to check the integrity of their data. Nevertheless, few schemes consider the fault's location function when the data auditing fails. Another problem is that anyone can make a challenge for the Cloud Server (CS) in the PDP schemes. Some access control strategies are necessary to reduce the waste of computation power resources of the CS. To solve these problems, we propose a certificateless and designed-verifier auditing scheme in multi-cloud storage environments. In our scheme, we utilize certificateless signature combined with a delegation key to achieve designed-verifier auditing. We design a secret Merkle Hash Tree (MHT) to locate the faults of CSs and data blocks. We utilize Zero-Knowledge Proof (ZKP) to achieve access control. Theoretical and experimental evaluation show that the proposed scheme is efficient and practical. [ABSTRACT FROM AUTHOR]

Medical errors can occur in many areas of healthcare, including hospitals, clinics, and surgery centers. They can result in negative consequences for patients and their loved ones. Over the years, different methods have been used to reduce medical errors. Zero Trust is an information security model that denies access to applications and data by default. Other industries have successfully used Zero Trust Model (ZTM), and it has been shown to improve outcomes. This editorial analyzes how the ZTM can be introduced to prevent medical errors in healthcare settings. ZTM application in healthcare could potentially revolutionize patient safety by tightly controlling and monitoring access to sensitive patient data and critical systems. By enhancing security measures, the ZTM could address the paramount concerns of patient data privacy and safety in healthcare. The zero-trust approach offers a potential solution by identifying consistent causes of errors and providing viable solutions to prevent their recurrence. In the era of worsening ransomware attacks on healthcare systems, the ZTM could also have enormous implications in other cybersecurity aspects. With this manuscript, the authors advocate for the broader application of ZTM across other facets of healthcare cybersecurity. [ABSTRACT FROM AUTHOR]

The Web 3.0 era is reshaping the way we share data, and online social networks play an essential role in this process. In online social networks (OSN), the interaction between users is closely related to data security. With the widespread use of OSN, the harm caused by fake news has penetrated many corners of society. Most existing graph machine learning works for fake news detection focus only on the news propagation path or news content itself; they ignore the trust relationships between users and the ease of attack of the graph formed by the propagation path. The underlying trust factors among users can be revealed by their endogenous preferences that help indicate the extent to which others are expected to perform particular actions. Moreover, joint trainable news propagation paths and social trust can improve the robustness of graph network models and slow the accumulation of errors caused by fraudulent messaging. However, these works are somewhat limited in fake news detection. This paper proposes novel robust trust evaluation architecture for fake news detection, RTrust, which improves the performance of fake news detection by incorporating trust propagation and robustness. Comparative results from the latest baseline on two real-world datasets demonstrate the advantages of RTrust in detecting fake news and its robustness. [ABSTRACT FROM AUTHOR]

The IoT, which refers to the process in which billions of devices are connected to the Internet, has emerged as an essential component of today's society. Yet, the safety of data exchange and the identification of devices in IoT networks have emerged as significant concerns in recent years. Also, there will be a significant drop in the effectiveness of the conventional consensus process. This study presents a novel consensus technique, Proof of IoT (PIoT), to address the security and authentication‐related issues in the IoT Infrastructure. It uses blockchain‐based smart contracts to securely transfer the data and uniquely identify the device on the IoT‐based network. PIoT uses a reputation assessment technique that involves complex cryptographic NuCypher‐based security computations to authenticate and update the data across IoT devices. This contract assures that only authorized parties can access the information recorded in the blockchain. A reward‐based filtering technique is developed for PIoT to identify malicious nodes with harmful actions, preventing them from contributing to consensus. Compared to specific conventional methods, PIoT performs better in simulations. Compared with LPBFT, DAC4SH Consensus, the Point of Interaction in blockchain‐based PIoT improved by 74.7% and 96.8%, respectively. When compared to DAC4SH, PIoT shows an overall 15% improvement in the security of the blockchain under the IoT. In addition to providing a secure method of communication between IoT devices, the given study also provides security against various IoT attacks. [ABSTRACT FROM AUTHOR]

In a world where technology is rapidly advancing, regulation of dark pattern practices has become a topic of increasing importance. Society has become somewhat desensitized to these deceptive online practices that manipulate users into taking actions, which are not in their best interests, such as difficulty unsubscribing from a service, prominence of consent buttons, and countless other advanced tactics to obscure transparency. However, these ongoing practices harm both the individual user, and society in general, by impeding informed decision‐making. This Article addresses the European Union's leading efforts to tackle dark pattern practices, and in particular, addresses the numerous legislative acts which have been enacted to regulate and eliminate them. The acts explored in this Article include the General Data Protection Regulation, the Uniform Commercial Practices Directive, the Data Act, the Digital Markets Act, the Digital Services Act, the Amendment to the Directive on Financial Services Contracts Concluded at a Distance, and the Artificial Intelligence Act. This Article then discusses the interplay between the numerous acts, and the resulting ambiguities and overlap which have led to a level of regulatory redundancy. This Article examines not only the difficulty in interpretation of the various acts, but additionally, explores the issues which arise in implementation from a jurisdictional perspective. Further, this Article suggests potential solutions to address the fragmented legislation, including a hybrid form of harmonization, as well as methods for consolidation and centralization. [ABSTRACT FROM AUTHOR]

In today's digitally connected world, the protection of privacy and data security in open communication media (OCM) is paramount. As digital devices become more widespread and increase in global connectivity, the significance of image encryption has reached unprecedented levels. Traditional encryption techniques often fall short when applied to images due to their unique properties such as high correlation and large data quantities. To address these challenges, this paper presents a novel image encryption method that integrates principles from deoxyribonucleic acid (DNA) rules, the logistic map, and a hyperchaotic model with memcapacitors. The intricate and unpredictable nature of the memcapacitor‐based hyperchaotic system, coupled with deficiencies in current image encryption algorithms, serves as a catalyst for the development of this new algorithm. The proposed encryption strategy leverages the encoding properties of DNA and the complex dynamics of hyperchaotic systems to achieve enhanced image encryption. Through simulations and evaluations using reference images, the effectiveness of the method is demonstrated. It surpasses current techniques in terms of statistical robustness, resistance to brute‐force attacks, and protection against differential attacks. The research concludes that combining DNA principles, logistic maps, and memcapacitor‐based hyperchaotic systems significantly improves image encryption. This approach enhances data security for image transmission in open communication channels, addressing the limitations of traditional encryption techniques. As digital connectivity expands globally, the demand for robust image encryption is expected to increase, making this research pivotal in establishing standards for future advancements in image encryption algorithms. [ABSTRACT FROM AUTHOR]

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services. [ABSTRACT FROM AUTHOR]

An introduction to articles in the issue is presented on topics including the scarcity of disordered speech samples for model training which is an obstacle to developing artificial intelligence (AI)-driven assistive technologies for individuals with speech disabilities, automatic speech recognition (ASR) systems that can be used in real-time conversations by these individuals, and challenges to advance ASR communication tools for those with speech impairments.

This research displays inspect a study on the classification of human skin diseases using medical imaging, with a focus on data privacy preservation. Skin disease diagnosis is primarily done visually and can be challenging due to variant colors and complex formation of diseases. The proposed solution involves an image dataset with seven classes of skin disease, a convolutional neural network (CNN) model, and image augmentation to increase dataset size and model generalization. The suggested CNN model attained an average precision of 86% and an average recall of 81% for all seven classes of skin diseases. To safeguard the privacy of the data, a federated learning method was used, in which the information was split among 500, 1000, and 2000 users. With the proposed scheme which based on CNN for disease classification and the federated learning method, the average accuracy was 82.42%, 87.26%, and 93.25% for the different numbers of clients. The findings show that it may be possible to effectively categorize skin illnesses by employing a CNN-based approach coupled with federated learning in order to achieve this goal. This would be conducted without compromising the confidentiality of patient data. [ABSTRACT FROM AUTHOR]

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care. [ABSTRACT FROM AUTHOR]

Swarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV‐to‐UAV communication, support for perfect forward secrecy and non‐repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token‐based protocol (ASMTP). The proposed protocol secures UAV‐to‐base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to provide a unified framework of key stakeholder action-oriented approaches to extended producer responsibility based on behavioral estimates of consumers. For Twitter data, descriptive analysis and content analysis (thematic modeling) were performed after reviewing the literature. In addition, a new text analysis framework was employed to analyze extended producer responsibility reports. A total of 2,561,216 tweets and 708 documents related to producer responsibilities were analyzed. According to descriptive analysis, tweets related to North American countries represent about 35 percent of Twitter activity, while tweets related to African countries represent about 5 percent. A further consideration is the amount of attention paid by policymakers to issues related to mobile phones and environmental concerns among young people (about 5% in the 10–20 age group and 15% in the 20–30 age group). Consumer participation is significantly influenced by social factors such as education, awareness, and trust. In addition, consumers are increasingly concerned about data security and privacy. Also, cost, economic benefits, proximity, and convenience influenced consumer participation. The second part of the analysis revealed that producers are attending to enhance the economic benefits of recovery, develop infrastructure (for consumer convenience), ensure data security and privacy for participants, and increase knowledge and awareness about recycling and recovery. The developed countries also work with producers to increase engagement, while developing countries are trying to overcome information transparency, a lack of economic potential, and public awareness. [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]

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate. [ABSTRACT FROM AUTHOR]

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability--core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system's capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies. [ABSTRACT FROM AUTHOR]

In the backdrop of the escalating incidents of mobile device theft and associated security challenges, a resilient and innovative solution is imperative. The traditional security mechanisms, largely reliant on the International Mobile Equipment Identity (IMEI), have been fraught with vulnerabilities, leading to a surge in incidents of device theft and data breaches. Addressing this pressing issue, we present a novel, two-tiered approach integrating sensor technology and blockchain to bolster mobile device security. This work aims to create and assess a dual-layered security strategy that uses blockchain and sensor technologies in a complementary way. Based on a rigorous conceptual framework, it explores a two-tiered security model that intricately combines sensor and blockchain technologies. This collaborative integration aims to provide an effective solution to the widespread challenges of theft and security breaches in mobile devices. The methodology employs a sensor layer for real-time data collection and processing to detect potential thefts, and activating alerts. The blockchain layer, invoked upon these alerts, initiates secure, transparent, and decentralized transactions for verification and validation across network nodes. This dual mechanism ensures swift and secure anti-theft actions, supported by an enhanced encryption standard. Our result analysis reveals the proposed system's superiority in computational time, energy consumption, and overall security levels when compared to existing protocols. The integration of real-time processing and blockchain's immutable nature promises reduced false positives and enhanced data integrity. The findings indicate that this integrative approach not only mitigates theft but also ensures data security, marking a significant stride in mobile security technology. In conclusion, this two-layered system promises a scalable, efficient, and robust solution to mobile device theft and data breaches, with potential impacts transcending individual device security to influence broader data privacy and security paradigms, thus signifying a pivotal development in the field of mobile security. [ABSTRACT FROM AUTHOR]

The technology-driven modern age emphasizes the security and privacy of communication. Through this paper, we delve deep into the need for user-centric security within cloud-based environments. The need for enhancement in encryption arises due to the increasing cases of data breaches and insider threats in cloud-based environments recently. The focus is laid upon the use of RSA encryption, end-to-end encryption, and anonymous messaging to address security-based concerns. The primary focus of this research is to develop a comprehensive security system to ensure the confidentiality and authenticity of text-based messages shared in the cloud. The proposed improved RSA algorithm, as suggested, incorporates three prime numbers in the key generation process. To address repetitive ciphertext, the proposed algorithm involves adding the index of each character in the plaintext string to the character's integer value before encryption. Conversely, during decryption, the same index is subtracted. This proposed algorithm has been utilized in a practical scenario, specifically in the implementation of a chat application. This paper presents a proof-of-concept for the proposed enhanced version of the RSA algorithm, accompanied by a thorough comparison and analysis of computational times across various bit lengths. Increase in data security at a cost of minor increase in computation time was observed through this research. [ABSTRACT FROM AUTHOR]

Purpose: This scoping review sought to understand the current knowledge on using digital signatures in implemented electronic health records (EHR). Methods: Our research was carried out on ACM Digital Library, ArXiv, Embase, Engineering Village, IEEE Digital Library, LILACS, Livivo, PubMed/MEDLINE, Scopus, and Web of Science, and gray literature was searched on Google Scholar and ProQuest Theses and Dissertations. Studies related to and associated with using digital signatures and their different types of extensions and forms of storage in EHR worldwide were included. Six authors independently collected the information and assessed the methodological quality of the studies, which quality was evaluated using the JBI Critical Appraisal Checklist. Results: A total of 8,834 studies were initially identified, and 4,592 remained after excluding the duplicated records. Ninety-three studies were selected for the full-text screening phase, and nine studies were included for analysis following the eligibility criteria. Five studies were limited to drug prescriptions, two were associated with using image records and two studies did not specify the digital signature use purpose. The most cited signature storage format was XML. The included studies addressed six signature compression technologies, such as COPRIS and LINCOS, ECDSA, IPFS, and JPEG 2000. Regarding signature storage architecture, seven studies reported remote storage, while only one used local storage. Other two studies associated local and remote storage with in-app storage. Conclusions: Despite the numerous advantages of EHR in health institutions, file security should be a priority in system development, and it still needs to evolve for safe and efficient health data usage. Further research is recommended for developing digital signatures, considering factors such as the time required to store health files. [ABSTRACT FROM AUTHOR]

Cloud computing enables efficient management and storage of large data volumes, providing scalable resources and effective solutions for handling extensive data. However, the cloud requires attention in the field of data security, reliability and privacy. To ensure that entire data is secured, cloud security handles all devices, apps and data that are integrated into the cloud. To overcome these problems, the encryption of cloud data is an effective way to secure the data. The Elliptic Curve Cryptography (ECC) and Password based Key Derivation Function (PKDF) with Advanced Encryption Standard (AES) method is proposed for effective cloud data security. Initially, the key generation by Edward Elliptic Curve and the key exchange is performed by using Diffie-Hellman Key Exchange (DHKE). Then, keys are generated for the client's password using the PKDF method and the encryption and decryption of data is performed by using the AES method which effectively secures the data in the cloud through encryption and decryption of data. The ECC and PKDF with the AES method require less encryption time of 0.006s and a decryption time of 0.005ms on 5 KB when compared with Hybrid Elliptic Curve Cryptography (HECC). Cloud computing enables efficient management and storage of large data volumes, providing scalable resources and effective solutions for handling extensive datasets. [ABSTRACT FROM AUTHOR]

Hypertension, which stands as a leading global health challenge, demands a dynamic approach for its effective management. The traditional methods of managing hypertension, centered on periodic clinic visits for blood pressure measurement and pharmacological interventions, are increasingly being complemented and enhanced by digital technologies. The integration of wearable devices, mobile applications, personalized treatments, and telehealth solutions into healthcare system is reshaping traditional hypertension care. Digitalization of hypertension management extends to population health, in addition to individual patient benefits, aimed at preventing and controlling hypertension on a broader scale. However, this digital revolution in hypertension management brings forth challenges related to data security, data accuracy, equitable access, and standardization of devices by international regulatory agencies. Addressing these issues is equally important to ensure that the benefits of digital technologies are accessible to everyone, irrespective of socio-economic factors. This paper concludes with a forward-looking perspectives, emphasizing the potential of digitalization to modify the landscape of hypertension management. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive bibliometric analysis of ChatGPT's impact on three critical sectors: healthcare, financial services, and education, covering the period from 2022 to 2024. The research aimed to explore the evolving trends, limitations, and ethical considerations associated with the deployment of ChatGPT and similar AI-driven conversational interfaces. The findings reveal that while ChatGPT offers significant potential in enhancing efficiency and decision-making processes, it also faces substantial challenges in data privacy, integration with existing systems, and the need for improved emotional intelligence. The study highlights the inherent biases in AI models, raising concerns about the accuracy and fairness of AI-driven outputs. Ethical considerations, including data privacy, transparency, accountability, and equity in AI applications, are identified as crucial areas requiring urgent attention and development. Additionally, the research underscores the importance of developing robust regulatory frameworks and ethical guidelines to govern AI deployment in these sectors. Successful integration of ChatGPT and similar technologies depends on addressing these challenges and ethical considerations through collaborative efforts among AI developers, industry professionals, policymakers, and ethicists. Future research should focus on improving AI's data security, emotional intelligence, bias reduction, human-AI collaboration, and assessing long-term impacts across diverse settings. [ABSTRACT FROM AUTHOR]

In studying ample data security in universities, it is essential to protect the privacy and integrity of data. Current schemes utilize blockchain technology for data sharing, where cloud servers are responsible for storing ciphertext and performing partial encryption and decryption calculations. Data encryption technology can effectively prevent hacker attacks and ensure the integrity of data transmission. However, cloud servers that are only partially trusted may tamper with the ciphertext or return incorrect results. To solve these problems, this paper improves the privacy of access policy, attribute revocation and data authenticity of traditional CP-ABE algorithm. A protection scheme for ample public data security in universities is proposed, and a decentralized network structure based on multiple nodes is designed to improve the efficiency of data queries. The simulation results show that the scheme performs well in the scenario where the number of system attributes is moderate and the complexity of the access policy is not high. [ABSTRACT FROM AUTHOR]

Nanophotonics techniques, driven by optical microscopy, enable high‐capacity data recording and readout, yet ensuring data security demands versatile, multi‐stimuli activatable media. Lanthanide ion‐doped upconversion nanoparticles hold promise for these applications by fine‐tuning upconversion luminescence emission. While chemical methods customize this emission with meticulous adjustments, optical methods face challenges such as high laser beam intensities, setup complexity, and wavelength limitations. Efficiently modulating upconversion luminescence emission requires multi‐stimuli methods or integrating upconversion nanoparticles with responsive materials. Here, dual‐stimulus thermo‐optical activation and optical microscopy are used to achieve sub‐micrometer ultrahigh upconversion luminescence emission tuning in newly developed hybrid organic–inorganic upconversion nanocomposites for high‐capacity and secure data storage and anticounterfeiting. Thermal stimulus (activation I) facilitates a 102‐fold increase in absorption, promoting the formation of an inorganic network and complex. Optical stimulus via focused 460‐nm femtosecond laser beam irradiation (activation II) enhances absorption by fivefold through sub‐micrometer photo‐polymerization bit recording. The method achieves a 92% decrease of 450‐nm upconversion luminescence emission intensity after activation I and over 90% quenching of this emission after activation II, enabling sub‐micrometer bit readout under 980‐nm continuous‐wave excitation laser beam irradiation. This work demonstrates the potential for high‐capacity and secure data storage, anticounterfeiting, high‐security encryption, and high‐resolution display. [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 success of deep learning (DL) relies heavily on training data from which DL models encapsulate information. Consequently, the development and deployment of DL models expose data to potential privacy breaches, which are particularly critical in data-sensitive contexts like medicine. We propose a new technique named DiffGuard that generates realistic and diverse synthetic medical images with annotations, even indistinguishable for experts, to replace real data for DL model training, which cuts off their direct connection and enhances privacy safety. We demonstrate that DiffGuard enhances privacy safety with much less data leakage and better resistance against privacy attacks on data and model. It also improves the accuracy and generalizability of DL models for segmentation and classification of mediastinal neoplasms in multi-center evaluation. We expect that our solution would enlighten the road to privacy-preserving DL for precision medicine, promote data and model sharing, and inspire more innovation on artificial-intelligence-generated-content technologies for medicine. [ABSTRACT FROM AUTHOR]

The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113 dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration. [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]

Large language models (LLMs) have brought significant advancements to artificial intelligence, particularly in understanding and generating human language. However, concerns over management burden and data security have grown alongside their capabilities. To solve the problem, we design a blockchain‐based distributed LLM framework, where LLM works in the distributed mode and its outputs can be stored and verified on a blockchain to ensure integrity, transparency, and traceability. In addition, a multiparty signature‐based authentication mechanism is necessary to ensure stakeholder consensus before publication. To address these requirements, we propose a threshold elliptic curve digital signature algorithm that counters malicious adversaries in environments with three or more participants. Our approach relies on discrete logarithmic zero‐knowledge proofs and Feldman verifiable secret sharing, reducing complexity by forgoing multiplication triple protocols. When compared with some related schemes, this optimization speeds up both the key generation and signing phases with constant rounds while maintaining security against malicious adversaries. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems. [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]

Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework's effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup. [ABSTRACT FROM AUTHOR]

Background: The healthcare sector is currently undergoing a significant transformation, driven by an increased utilization of data. In this evolving landscape, surveys are of pivotal importance to the comprehension of patient needs and preferences. Moreover, the digital affinity of patients and physicians within the healthcare system is reforming the manner in which healthcare services are accessed and delivered. The utilization and donation of data are influencing the future of medical research and treatment, while artificial intelligence (AI) is empowering patients and physicians with knowledge and improving healthcare delivery. Methods: In order to evaluate the opinions of patients and physicians regarding the management of personal health data and the functionality of upcoming data management devices in the context of healthcare digitization, we conducted an exploratory study and designed a survey. The survey focused on a number of key areas, including demographics, experience with digitization, data handling, the identification of needs for upcoming digitization, and AI in healthcare. Results: A total of 40 patients and 15 physicians participated in the survey. The results indicate that data security, timesaving/administrative support, and digital communication are aspects that patients associate with patient-friendly digitization. Based on the responses provided by physicians, it might be concluded that future digital platforms should prioritize usability, time efficacy, data security, and interoperability. Conclusions: In terms of expectations for future digital platforms, there is a notable overlap between the needs expressed by patients and those identified by physicians, particularly in relation to usability, time management, data security, and digital communication. This suggests that the requirements of different stakeholders can be combined in a future system, although individual issues may still require attention. [ABSTRACT FROM AUTHOR]

Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development. [ABSTRACT FROM AUTHOR]

The connected and automated vehicles (CAVs) that are expected to be increasingly common on U.S. roads in the coming decades offer potential benefits in safety, efficiency, and mobility; they also raise concerns related to equity, access, and impacts on land use and travel behavior, as well as questions about extensive data requirements for CAVs to communicate with other vehicles and the environment in order to operate safely and efficiently. We report on interviews with North Carolina transportation experts about CAVs and their implications for sustainable transportation that serves all travelers with affordable, safe, and dignified mobility that also produces fewer environment impacts (emissions to air, water, and land; resource consumption; land use changes). The data reveal great interest among transportation professionals about a CAV transition, but a lack of consensus on the state of play and necessary next steps. Concerns include impacts on planning practice; implications for land use, equity, and safety; and data security and privacy. The findings suggest that local, regional, and state agencies would benefit from clear technical guidance on how to prepare for CAVs and to engage with the public, given high interest about a coming CAV transition. Intense data requirements for CAVs and associated infrastructure, as well as the regulatory and policy tools that will be required, raise concerns about threats to data safety and security and argue for proactive action. [ABSTRACT FROM AUTHOR]