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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]

As the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future. [ABSTRACT FROM AUTHOR]

The recent Federal Trade Commission (FTC) and Department of Justice (DOJ) action against Monument Inc. for selling patient data, is just the tip of the iceberg. Monument Inc. positions itself as a treatment provider (the website home page says it provides "therapy" for alcoholism) but in its extensive terms of use, it says it is not a treatment provider. And in fact, since the FTC/DOJ action this spring, Monument radically changed its website, removing the offending promise of "confidentiality" which it breached, but still selling services (lowest price $15/month). [ABSTRACT FROM AUTHOR]

To determine needs, expectations, facilitators, and barriers of insurance physicians (IPs) for using eHealth in their work. Also, we investigated differences between age groups. All insurance physicians employed at the Dutch Social Security Institute (SSI) received an online anonymous survey in July 2020. Three hundred and fifteen IPs (31%) responded. According to these IPs, the most important need for using eHealth was to collect medical information more effectively and efficiently (71%). Main facilitators were that eHealth could make IPs' work more effectively and efficiently (61%) and more future-proof (60%). Main barriers were losing human interaction (54%) and security issues (51%). Younger IPs saw more options for using eHealth, compared to older IPs. The majority of IPs (in particular younger IPs) had a positive view towards using eHealth in their daily work. Nevertheless, differences in needs, expectations, facilitators and barriers between the age groups should be taken into account for the successful development and implementation of interventions using eHealth in insurance medicine. For the successful development and implementation of eHealth interventions in insurance medicine and rehabilitation, the needs, expectations, facilitators, and barriers that physicians indicate should be taken into account. Insurance physicians support the use of eHealth interventions to collect medical information more effectively and efficiently, to contribute to and maintain the quality of care, in the perspective of managing expected shortages in insurance physicians. When concretizing eHealth interventions for rehabilitation professionals, one should take the importance of face to face interaction with patients into account. Extra education and training for older insurance physicians may improve the implementation of eHealth interventions, because they are less inclined to see its value and feel less competent to use it. [ABSTRACT FROM AUTHOR]

An AI-empowered indoor digital contact-tracing systemwas developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Zero watermarking constructs the watermark information according to the characteristics of the original data, without changing the data structure and data accuracy. Maintaining high data accuracy is the premise of building information modelling (BIM) usability, so zero watermarking is a hotspot in the research of BIM data security protection. BIM model is a type of 3D model, however, most of the existing zero watermarking algorithms for 3D models are difficult to be better applied to BIM data due to data structure differences. To solve this problem, a zero-watermarking algorithm for BIM data based on distance partitioning and skewness measure is proposed. Firstly, after spatial partitioning based on element paradigm value, the mapping relationship between different partitions and watermarking bits is established. Then, the skewness of elements is calculated, and the skewness measure sign is used as the feature to obtain the binary sequence. Finally, the dissimilarity operation is performed on the binary sequence and the original watermarking sequence which was disordered to construct the zero watermark of the BIM data. The experimental results show that the zero watermarks constructed from different BIM data are unique and robust to translation, rotation, element deletion, element addition, and format conversion attacks. In addition, the superiority of this paper's algorithm over the comparison algorithm in terms of robustness is compared. Therefore, the proposed algorithm can effectively provide technical support for BIM data copyright protection. [ABSTRACT FROM AUTHOR]

Background: Low-quality data presents a significant challenge for community health workers (CHWs) in low and middle-income countries (LMICs). Mobile health (mHealth) applications offer a solution by enabling CHWs to record and submit data electronically. However, the barriers and benefits of mHealth usage among CHWs in informal urban settlements remain poorly understood. This study sought to determine the barriers and benefits of mHealth among CHWs in Banda parish, Kampala. Methods: This qualitative study involved 12 key informant interviews (KIIs) among focal persons from Kampala City Council Authority (KCCA) and NGOs involved in data collected by CHWs, and officials from the Ministry of Health (MOH) and two mixed-sex Focused Group Discussions (FGDs) of CHWs from Banda parish, Kampala district. Data analysis utilised Atlas Ti Version 7.5.7. Thematic analysis was conducted, and themes were aligned with the social-ecological model. Results: Three themes of institutional and policy, community and interpersonal, and individual aligning to the Social ecological model highlighted the factors contributing to barriers and the benefits of mHealth among CHWs for iCCM. The key barriers to usability, acceptability and sustainability included high training costs, CHW demotivation, infrastructure limitations, data security concerns, community awareness deficits, and skill deficiencies. Conversely, mHealth offers benefits such as timely data submission, enhanced data quality, geo-mapping capabilities, improved CHW performance monitoring, community health surveillance, cost-effective reporting, and CHW empowering with technology. Conclusion: Despite limited mHealth experience, CHWs expressed enthusiasm for its potential. Implementation was viewed as a solution to multiple challenges, facilitating access to health information, efficient data reporting, and administrative processes, particularly in resource-constrained settings. Successful mHealth implementation requires addressing CHWs' demotivation, ensuring reliable power and network connectivity, and enhancing capacity for digital data ethics and management. By overcoming these barriers, mHealth can significantly enhance healthcare delivery at the community level, leveraging technology to optimize resource utilization and improve health outcomes. mHealth holds promise for transforming CHW practices, yet its effective integration necessitates targeted interventions to address systemic challenges and ensure sustainable implementation in LMIC contexts. [ABSTRACT FROM AUTHOR]

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange. [ABSTRACT FROM AUTHOR]

E-commerce platforms incorporate reputation systems that allow buyers to rate sellers after transactions. However, existing reputation systems face challenges such as privacy leakage, linkability, and multiple rating attacks. The feedback data can inadvertently expose user information privacy because they reveal the buyers' identities and preferences, which deters a significant number of users from providing their ratings. Moreover, malicious actors can exploit data analysis and machine learning techniques to mine user privacy from the rating data, posing serious threats to user security and trust. This study introduces ARS-Chain, a pioneering and secure blockchain-driven anonymous reputation-sharing framework tailored for e-commerce platforms. The core of ARS-Chain is a dynamic ring addition mechanism with linkable ring signatures (LRS), where the number of LRS rings is dynamically added in alignment with the evolving purchase list, and LRS link tags are constructed with the LRS rings and item identifiers. Further, a consortium blockchain is introduced to store these anonymous ratings on e-commerce platforms. As a result, ARS-Chain ensures full anonymity while achieving cross-platform reputation sharing, making rating records unlinkable, and effectively countering multiple rating attacks. The experimental results confirm that ARS-Chain significantly enhances user information privacy protection while maintaining system performance, having an important impact on the construction of trust mechanisms for e-commerce platforms. [ABSTRACT FROM AUTHOR]

The article concentrates on exploring the issue of privacy‐preserving sliding mode consensus of multi‐agent systems (MASs) with disturbance. An encryption and decryption algorithm has been proposed to address data security and privacy issues during data transmission. To optimize network resource allocation, a dynamic event‐triggering mechanism has been introduced, which reduces the number of encrypted data while saving the computation cost. The consensus performance based on the sliding mode control strategy is achieved when the reachability of the slide‐mode surface is guaranteed, and then the slide‐mode controller is developed. Finally, an empirical demonstration through a numerical example validates the efficacy of the proposed strategy. [ABSTRACT FROM AUTHOR]

In the modern day, multimedia and digital resources play a crucial role in demystifying complex topics and improving communication. Additionally, images, videos, and documents speed data administration, fostering both individual and organizational efficiency. Healthcare providers use tools like X-rays, MRIs, and CT scans to improve diagnostic and therapeutic capacities, highlighting the importance of these tools in contemporary communication, data processing, and healthcare. Protecting medical data becomes essential for maintaining patient confidentiality and service dependability in a time when digital assets are crucial to the healthcare industry. In order to overcome this issue, this study analyses the DWT-HD-SVD algorithm-based invisible watermarking in medical data. The main goal is to verify medical data by looking at a DWT-based hybrid technique used on X-ray images with various watermark sizes (256*256, 128*128, 64*64). The algorithm's imperceptibility and robustness are examined using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and are analyzed using Normalized Connection (NC), Bit Error Rate (BER), and Bit Error Rate (BCR) in order to evaluate its resistance to various attacks. The results show that the method works better with smaller watermark sizes than it does with larger ones. [ABSTRACT FROM AUTHOR]

Summary: Communicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity. [ABSTRACT FROM AUTHOR]

According to Thelwall's ([2011]. "Privacy and Gender in the Social Web." In Privacy Online. Perspectives on Privacy and Self-Disclosure in the Social Web, edited by S. Trepte and L. Reinecke, 251–266. Springer) social web gendered privacy model, gender differences in offline privacy risks (i.e. experiences of privacy threat, such as aggressive behaviour or betrayal of a secret) and communication qualities transfer to online contexts, and shape gender differences in online privacy perceptions and behaviours. Using representative data (n = 1,043) from four times of measurement over the course of three years, a structural equation model was set up. I found that people with negative offline privacy experiences at T1 express higher online privacy concerns a year later (T2), and take more actions to protect their online privacy at T3. When adequate privacy protection is established, people disclose more personal information privately (e.g. messenger), but not in public (e.g. status updates) at T4. Females reported more negative offline privacy experiences, offline social support, and offline information disclosure. In contrast to the model's claims, in an online context, men disclose more personal information both privately and publicly. The results provide evidence for the proposed relations of Thelwall's (2011) model: Offline conditions transfer to online contexts and shape social media users' privacy perception. However, the findings do not support the idea that women are an especially vulnerable group in online settings. [ABSTRACT FROM AUTHOR]

Data users (researchers, scientists) in healthcare institutions need access to integrated healthcare data to conduct timely analysis of diseases to serve the right population at the right time. However, preserving patient privacy and timely access to quality healthcare data is a critical challenge. Current healthcare data governance systems are largely manual. Besides, processing process data requests is extremely slow, often taking months. To address this gap, we designed an honest-broker-based healthcare application to support data users in accessing healthcare data securely and to design a comprehendible process of data governance for data users. This study applied two iterations of a user experience (UX) evaluation of an honest broker prototype. Results show that participants found the new system promising for their research prospects. Implications suggest that technological knowledge should not be a requirement for using healthcare applications to promote broader adoption in the community. This study highlights the necessity of a process to balance the control of access to sensitive data between data providers and users as well as to educate data users on data privacy. Iterative UX studies can be a fruitful approach in gradually uncovering problems and improving the design of complex systems. [ABSTRACT FROM AUTHOR]

Background and Objectives Social well-being of older adults living in low-income housing was disproportionately affected by the coronavirus disease 2019 pandemic. We explored low-income residents' experiences of social isolation and loneliness and strategies to remain socially connected during the pandemic. Research Design and Methods As part of a larger, 3-phase user-centered design study, we conducted a qualitative study using focus groups to gain insights into social isolation experiences and the role of information and communication technologies (ICTs), including smart speakers, in social connectedness (N  = 25, 76% African American). We also collected survey data to describe social isolation, loneliness, and current ICT use in the sample. Participants included both smart speaker users and nonusers. Results Experiences of social isolation and loneliness varied by participants' sociodemographic characteristics and previous experiences with smart speakers. Qualitative analysis showed participants demonstrated coping strategies developed during the pandemic to adapt to new norms of connecting with others, including technology-enabled social interactions. Participants expressed a strong desire to build community together in their facilities and highlighted the potential role of smart speakers in making meaningful social connections, encompassing safety checks to have a means for emergencies, and providing a virtual companion. Access, digital literacy, training, security, and privacy issues were discussed as factors affecting their adoption of new ICT for enhanced social connectedness. Discussion and Implications This study highlights the importance of understanding the unique social isolation experience, demographics, and social determinants of health of low-income residents to develop ICT-based interventions for social connectedness. [ABSTRACT FROM AUTHOR]

The article discusses the ethical issues surrounding telesurgery, including its precise definition, concerns about equitable access to technology, and potential conflicts of interest in marketing and financial incentives. It reports the improved patient outcomes, it also highlights the need for accurate marketing and patient-centered decision-making, despite potential misleading marketing.

High leakage power consumption has become one of the main concerns of data security protection with CMOS device scaling. Spintronic technology is one of the efficient solutions to control circuit leakage power consumption by benefiting from its non-volatility property. Spintronic devices such as magnetic tunnel junctions (MTJs) are also compatible with CMOS transistors and suitable for designing hybrid MTJ/CMOS circuits. Hybrid MTJ/CMOS circuits can be used in many applications, such as logic-in-memory (LiM) and high-security structures. These circuits demonstrate enhanced data security compared to CMOS-based circuits by leveraging their non-volatile feature, making information leakage and data access more challenging. However, these circuits are not completely immune to attacks, and information can be revealed by potent power analysis attacks such as differential power analysis (DPA) and correlation power analysis (CPA). This paper investigates the resistance to side-channel attacks, especially DPA and CPA, for the magnetoresistive RAM (MRAM) and hybrid MTJ/CMOS AND, OR, XOR gates, hybrid MTJ/CMOS full-adder, and multiplexer circuits. Comprehensive post-layout simulation results using 40 nm TSMC CMOS technology, Monte-Carlo results, and values of normalized energy deviation (NED), normalized standard deviation (NSD)), and normalized power deviation (NPD) of PCSA-based MRAM indicate that the power consumption pattern remains constant in circuits with symmetrical structures in the reading phase even in the presence of inevitable process variation, so these circuits are resilient to power attacks and are competent for use in high-security applications such as hardware implementation of encryption algorithms. [ABSTRACT FROM AUTHOR]

Recently, several private and political cloud initiatives emerged in Europe. This paper demonstrates how the sociotechnical imaginaries of three European cloud projects reveal a performative coupling of innovation and political ideas of control, territoriality and sovereignty. I ascertain three elements of the concept of sociotechnical imaginaries (innovation, boundary making and material properties) guiding the empirical analysis. Taking technology in the making and its role in (geo)politics seriously, this paper shows how imaginaries shape and interact with current geostrategic and political developments in Europe. The analysis of Microsoft's cloud, Bundescloud and GAIA-X reveals that rising privacy and data security issues have been integrated into cloud imaginaries that traditionally highlight progress and innovation. More specifically, state actors and cloud providers link and sometimes merge allegedly opposing technological aspects of innovation and politicised ideas of control such as digital sovereignty. This shift constitutes a move towards erecting political borders and localising IT within a global infrastructure. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

The interplanetary File system (IPFS) provides data communication using plaintext over distributed and decentralized systems. Up till now, the security of data has mostly been established using keys. To facilitate classified data communication within a Closed User Group (CUG), services of IPFS private swarm are required to render additional content encryption. Currently, there is no inherent security built into IPFS. This paper presents an application DMaya on IPFS to provide keyless encryption on IPFS based on the Quasigroup-based Secret Sharing Scheme (QSS). IPFS is used as a storage medium, irrespective of the nature of data. Data security in decentralized mode with IPFS is provided with DMaya. The classified data of all types (i.e. video data, audio data, text data, and image data) when subjected to DMaya application, are fragmented into smaller fragments then processed by the probabilistic Quasigroup-based Secret Sharing (QSS) Scheme. DMaya keyless encryption generates the metadata tag file at the source node. The recipient node within the same private IPFS swarm, on receipt of metadata tag file reconstructs the original data with DMaya application. [ABSTRACT FROM AUTHOR]

Objective: This article describes the first digital clinical information system tailored to support the operational needs of a forensic unit in Greece and to maintain its archives. Method: The development of our system was initiated towards the end of 2018, as a close collaboration between the Medical School of the University of Crete and the Forensic Medicine Unit of the University Hospital of Heraklion, Crete, where forensic pathologists assumed active roles during the specification and testing of the system. Results: The final prototype of the system was able to manage the life cycle of any forensic case by allowing users to create new records, assign them to forensic pathologists, upload reports, multimedia and any required files; mark the end of processing, issue certificates or appropriate legal documents, produce reports and generate statistics. For the first 4 years of digitised data (2017–2021), the system recorded 2936 forensic examinations categorised as 106 crime scene investigations, 259 external examinations, 912 autopsies, 102 post-mortem CT examinations, 804 histological examinations, 116 clinical examinations, 12 anthropological examinations and 625 embalmings. Conclusion: This research represents the first systematic effort to record forensic cases through a digital clinical information system in Greece, and to demonstrate its effectiveness, daily usability and vast potential for data extraction and for future research. [ABSTRACT FROM AUTHOR]

Electromagnetic camouflage is crucial for concealing the physical identity of the object from external electromagnetic detection, whether in military operations or civilian contexts. However, addressing the pressing issue of seamlessly transitioning between camouflage and visualization capabilities is a significant challenge. By combining chiral meta‐atoms and irregular phase encoding method, a novel chiral meta‐coder is proposed for spin switchable electromagnetic camouflage and target authentication. Different from traditional conformal strategies, this meta‐coder achieves camouflage functionality by tightly arranging meta‐atoms around the target object and encoding suitable phase sequences to shape desired wavefront. Simultaneously, the target object can also be identified by modulating the helical state of the incident wave. As a proof of concept, a chiral meta‐coder operating in camouflaging "A" shaped target object is designed and experimentally demonstrated. When exposed to right circularly polarized waves, the meta‐atoms surrounding the object can adapt the wavefront to conceal target information. The camouflaged pattern exhibits diffuse scattering in the far field and three focal patterns around the near field. Conversely, these meta‐atoms transition into a wave‐absorbing configuration, facilitating the precise identification of target information. Such a strategy may find potential applications in data security, privacy, and communication. [ABSTRACT FROM AUTHOR]

Security and efficiency remain a serious concern for Internet of Things (IoT) environments due to the resource-constrained nature and wireless communication. Traditional schemes are based on the main mathematical operations, including pairing, pairing-based scalar multiplication, bilinear pairing, exponential operations, elliptic curve scalar multiplication, and point multiplication operations. These traditional operands are cost-intensive and require high computing power and bandwidth overload, thus affecting efficiency. Due to the cost-intensive nature and high resource requirements, traditional approaches are not feasible and are unsuitable for resource-limited IoT devices. Furthermore, the lack of essential security attributes in traditional schemes, such as unforgeability, public verifiability, non-repudiation, forward secrecy, and resistance to denial-of-service attacks, puts data security at high risk. To overcome these challenges, we have introduced a novel signcryption algorithm based on hyperelliptic curve divisor multiplication, which is much faster than other traditional mathematical operations. Hence, the proposed methodology is based on a hyperelliptic curve, due to which it has enhanced security with smaller key sizes that reduce computational complexity by 38.16% and communication complexity by 62.5%, providing a well-balanced solution by utilizing few resources while meeting the security and efficiency requirements of resource-constrained devices. The proposed strategy also involves formal security validation, which provides confidence for the proposed methodology in practical implementations. [ABSTRACT FROM AUTHOR]

Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times. [ABSTRACT FROM AUTHOR]

Improper processing of sensitive personal data concerning disabled people in government information disclosure not only violates their privacy rights but also leads to discrimination, stigmatization, and other serious secondary harm, that have long been overlooked. China's procurator-led public-interest litigation system is a powerful tool for protecting the privacy rights of vulnerable groups. This paper contends that procurator-led public-interest litigation, as a supplement to and support for private-interest litigation, assists the Chinese government in fulfilling its international obligations to protect the right to privacy of disabled people. [ABSTRACT FROM AUTHOR]

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence (AI) provides unprecedented opportunities to improve injury surveillance systems in many ways, including the curation and publication of information related to agricultural injuries and illnesses. This editorial explores the feasibility and implication of ChatGPT integration in an international sentinel agricultural injury surveillance system, AgInjuryNews, highlighting that AI integration may enhance workflows by reducing human and financial resources and increasing outputs. In the coming years, text intensive natural language reports in AgInjuryNews and similar systems could be a rich source for data for ChatGPT or other more customized and fine-tuned LLMs. By harnessing the capabilities of AI and NLP, teams could potentially streamline the process of data analysis, report generation, and public dissemination, ultimately contributing to improved agricultural injury prevention efforts, well beyond any manually driven efforts. [ABSTRACT FROM AUTHOR]

By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid's multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance. [ABSTRACT FROM AUTHOR]

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software's features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community's methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "pre-processing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Cloud computing has become increasingly popular due to its scalability, cost-effectiveness, and ability to handle large volumes of data. However, entrusting (sensitive) data to a third party raises concerns about data security and privacy. Homomorphic encryption is one solution that allows users to store and process data in a public cloud without the cloud provider having access to it. Currently, homomorphic encryption libraries only support addition and multiplication; other mathematical functions must be implemented by the user. To this end, we discuss and implement the division, exponential, square root, logarithm, minimum, and maximum function, using the CKKS cryptosystem of the OpenFHE library. To demonstrate that complex applications can be realized with this extended function set, we have used it to homomorphically realize the Box–Cox transform, which is used in many real-world applications, e.g., time-series forecasts. Our results show how the number of iterations required to achieve a given accuracy varies depending on the function. In addition, the execution time for each function is independent of the input and is in the range of ten seconds on a reference machine. With this work, we provide users with insights on how to extend the original restricted function set of the CKKS cryptosystem of the OpenFHE library with basic mathematical functions. [ABSTRACT FROM AUTHOR]

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to "mental privacy." In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum's "contextual integrity" theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. [ABSTRACT FROM AUTHOR]

ChatGPT is a chatbot that is based on the generative pretrained transformer architecture as an artificial inteligence-based large language model. Its widespread use in healthcare practice, research, and education seems to be (increasingly) inevitable. Also considering the relevant limitations regarding privacy, ethics, bias, legal, and validity, in this article, its use as a supplement (for sure not as a substitute for physicians) is discussed in light of the recent literature. Particularly, the "opinion" of ChatGPT about how it can help/harm physiatry is exemplified. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, Internet of Things technology has become a hot topic in today's society. This study aims to explore how IoT technology impacts various aspects of financial management, in particular how it changes the collection, processing, and parsing of financial data. Through in-depth literature review, questionnaire survey and experimental design, the research found that IoT technology has a significant positive effect on sales, transaction times and inventory control. But at the same time, data security, privacy concerns and initial technology investment are also challenges that enterprises need to consider when adopting these technologies. Overall, IoT technology offers tremendous opportunities for financial management, but businesses must fully assess the potential risks and benefits when implementing it. [ABSTRACT FROM AUTHOR]

To hide confidential messages from people who are not responsible or who can access the messages, a way is needed to hide the messages. One way to hide messages in transmission is to change the data into something unintelligible by encoding and embedding it using cryptography and steganography techniques. This application was built using the hill cipher algorithm and the Rivest Cipher 4 (RC4) method. This algorithm is a symmetric key algorithm which has several advantages in data encryption. The hill chiper algorithm uses a mxm matrix as the encryption and decryption key. Meanwhile, the RC4 symmetric key is in the form of a stream cipher which can process input data as well as messages or information. Input data is generally in the form of bytes or even bits. The results of this research show that hill cipher and RC4 have their respective advantages and disadvantages. However, currently, RC4 is generally considered less safe for use in securitycritical scenarios due to its vulnerability to attack. It is highly recommended to use an encryption algorithm such as advanced encryption standard (AES) which is modern and strong and has been tested and proven to be more resilient. [ABSTRACT FROM AUTHOR]

The study was conducted in Shebadino, Tula and Wondogenet districts of Sidama regional state of southern Ethiopia from 2018 to 2023 with the objectives of to evaluate the performance of silk production technologies at various levels of silk production with stakeholders/beneficiaries. The study also aimed to create awareness and develop confidence among silkworm producers, development agents, agricultural experts and policy makers for wider dissemination. Nine kebeles’ (the lowest administrative level), were selected from three (3) districts for the purposes of this study. Five (5) farmers from each kebele were selected, totaling 45 farmers, for technology pre-scale up and promotion. Three rounds of theoretical and practical training were given to these farmers after they were selected. Training in the third round focused on managing row silk and cocoons after harvest, with a first round that covered broad knowledge and the importance of sericulture. Of the farmers who were chosen and trained, 95.6 percent were women who began raising silk worms, producing cocoons, and processing row silk. Each farmer received 30 kilograms of castor seed and 50 disease-free adult moths for pre-scaling up and promotion of the technology. During the first year of the study period, 90%, 50%, and 40% of the disease-free moths that were disseminated laid eggs and developed into cocoons at Shebadino, Tula sub city and Wondogenet districts respectively. Sericulture is very beneficial to young people, particularly women, in terms of providing a variety of employment options and viable income without affecting their regular agricultural activities or the existing sociocultural equilibrium. Sericulture technology with its package used, knowledge and skill of the farmers (users) on the technology upgraded, continuous follow up assured by DAs and experts. This study highlights the significance of sericulture for rural development and offers insightful information for its dissemination. [ABSTRACT FROM AUTHOR]

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested. [ABSTRACT FROM AUTHOR]

To guarantee the integrity of cloud data, plenty of cloud storage auditing schemes are proposed. In cloud storage, when a company is purchased by another company, the corresponding data of the acquired company will be transferred to the acquiring company. In addition, there may be duplicate files between the acquiring company and the acquired company. To solve the above problems, we propose a secure cloud storage auditing scheme with deduplication and efficient data transfer. We design a novel signature transformation method, in which the signatures of the acquired company can be efficiently transformed into the signatures of the acquiring company with the assistance of the cloud. Using the above method, the acquiring company does not require to recompute the data signatures for the transferred data when cloud data is transferred. Furthermore, to improve the efficiency of cloud storage, we use data deduplication method, in which the cloud stores only a single copy of the duplicate files. The security proof and performance analysis show that the proposed scheme is secure and efficient. [ABSTRACT FROM AUTHOR]

Now that we live in the digital age, the proliferation of Internet of Things (IoT) strategies raises various design concerns related to privacy for businesses. Patients' medical data poses an ethical and legal quandary for healthcare organisations, making security a difficult problem to solve. Early studies suggest that blockchain technology could be a substantial answer to the IoT's data security issues. It is therefore critical to ensure data security when designing a blockchain approach for healthcare applications. A blockchain-based data broadcast strategy with a categorization model in the healthcare industry is proposed in this study. Data from multiple IoT data providers, like blockchain, is used to make secure training algorithms. To ensure a safe and secure learning environment, Homomorphic Encryption (HE) technology is used. The oppositional-based harmony search (OHS) algorithm was used to make the best key for the HE algorithm. For the group of multiple shares of acquired images, a multiple-share creation (MSC) ideal is used. This gives privacy and security to the model. In addition, the blockchain technology is used to transmit data securely to the cloud server, which performs the classification model based on the convolutional neural network to determine the presence of disease. To summarise, the proposed model is known as the OHE-MSC based network, and it employs blockchain technology to create a secure and trustworthy platform for the exchange of data between many data providers using IoT and logging it in a shared ledger. Two benchmark datasets, such as the Breast Cancer Wisconsin Dataset (BCWD) and the Heart Disease Data Set (HDD), are used to evaluate the projected method's performance. Several parts of the simulation showed that the OHE-MSC-based network model achieved better performance than all other approaches. [ABSTRACT FROM AUTHOR]

Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine. Methods: We undertook a qualitative interview‐based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented. Results: Twenty‐five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years. Conclusions: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations. This qualitative study found that emergency clinicians in Australia and New Zealand were generally optimistic about the use of artificial intelligence (AI)‐enabled decision support tools in EDs. A number of issues were raised that require attention before full acceptance. AI‐enabled tools must operate as decision support only, be locally validated, be effectively integrated into existing workflows and take ethical concerns into consideration. [ABSTRACT FROM AUTHOR]