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The concealment of secret information has become a significant concern in today's highly digitalized world due to the rapid increase in unauthorized data access and network policy violations. In response, steganography has emerged as an alternative technique for securing communication by embedding confidential information within digital files. This paper presents an enhanced scheme for hiding secret bits by utilizing fuzzy-detected edges and a modulus function applied to image pixels in the spatial domain. Unlike previous approaches that focused solely on concealing data in the image’s smooth areas with limited differences, neglecting other potential values, this method addresses these limitations by considering positive and negative difference values between adjacent pixels to hide the secret data effectively. Experimental results show an average improvement of 15% in peak signal-tonoise ratio (PSNR), indicating better stego image quality and a 20% increase in embedding capacity compared to existing benchmark methods. [ABSTRACT FROM AUTHOR]

Abstract The rapid growth of Information and communication technology not only has positive impacts but also unveils opportunities for data and information security threats. In recent years, many researchers have worked on developing methods to enhance data security, particularly through data hiding techniques aimed at safeguarding communications by concealing their existence. With the same objective in mind, this study introduces a novel method for Reversible Data Hiding (RDH) based on a combination of difference expansion (DE) and a modulus function. Our method enables the embedding of 3-bit data into 2-bit Least Significant Bit (LSB) difference values of pixel pairs formed in rectangular blocks. Based on the experimental results, the payload capacity of our method can reach 0.3953 bpp with a PSNR of 53.5900 dB on common images and 0.5764 bpp with a PSNR of 52.9234 dB on medical images. Our method consistently achieves high payload capacity with good visual quality, and our method surpasses previous approaches in terms of performance, payload capacity, and visual quality.

This study delves into the profound influence of artificial intelligence (AI) on the fiscal dynamics of the public sector. It chiefly examines how AI can enhance aspects such as efficiency, precision, openness, and responsibility in public financial administration. Literature research is included in qualitative research methods. To conduct Public Sector Finance literature. This research was conducted by the SLR method or systematic literature review by collecting data through the PRISMA method sourced from the Scopus database case studies focusing on AI deployment across various nations. Data It integrates both quantitative and qualitative data analysis to assess AI's impact and efficacy within the sphere of public finance. The final number of data was chosen 27 data to be selected. The primary discoveries reveal that AI harbors considerable potential to refine financial procedures in the public sector. This includes augmenting automation, diminishing human error, and bolstering decision-making grounded in data analysis. However, the study acknowledges challenges like data security concerns, technological infrastructure prerequisites, and the scarcity of adequately skilled personnel. It proposes resolutions such as establishing robust policy frameworks and implementing human resources training initiatives to surmount these hurdles. The implications of this research hold substantial significance for policymakers and practitioners in the public domain. AI is reshaping not just the operational modalities of public financial entities but also influencing governmental policy-making, strategies for human resource development, and the interaction dynamics between citizens and the government. The article underscores the necessity for a proactive and responsive adaptation of AI technologies, advocating for their integration to guarantee effective and transparent governance in public finance for the future.

The growing frequency of natural and manufactured catastrophes necessitates urgently developing efficient response systems to lessen their effects and save lives. This article provides a complete disaster response architecture based on the Internet of Things (IoT) and 5G technologies. The suggested paradigm tackles fundamental flaws in emergency response systems, such as delay, data overload, and fragmented communication routes. The framework attempts to develop a more coordinated, real-time response mechanism by exploiting the high-speed, low-latency capabilities of 5G networks and IoT devices' sensing and data-gathering capabilities. The framework's key components are as follows: advanced sensing capabilities for immediate and accurate data collection from affected areas, centralized data analytics to assess the severity and prioritize actions, secure and real-time communication channels for all stakeholders, and dynamic resource allocation based on evolving scenarios. Theseingredients work together to not only give instant comfort but also to initiate long-term healing processes quickly. The article examines the framework's technical design, data security problems, and prospective applications, including proof-of-concept demos and case studies. The integrated approach optimizes decision-making and increases the overall efficacy of disaster management techniques by making the best use of available resources. This study adds to continuing efforts to harness new technology for societal well-being and lays the groundwork for future disaster response system advancements.

Background: The digitization of healthcare data has made significant progress in medical research and personalized medicine. Nonetheless, conventional centralized data-sharing structures present primary obstacles to information privacy and security due to laws like the Health Insurance Portability and Accountability Act (HIPAA). Federated Learning (FL) has been proposed as a potential solution that can enable collaborative learning with decentralized datasets without the requirement of data centralization. Objective: This study examines how well Federated Learning performs in meeting both the requirements of secure data exchange between healthcare organizations and achieving high model accuracy without violating any privacy compliance regulations. Methods: A Federated Learning framework was implemented with a neural network model using federated learning on an electronic health records (EHR) database collected from multiple hospitals. Its accuracy was compared to a traditional centralized model across various states, while the people also consider its convergence speed and data leakage risks. We incorporated differential privacy mechanisms in order to improve the security of data sets as well as prevent malicious attacks. Results: The FL model achieved accuracy comparable to the centralized model, with only a marginal reduction. Furthermore, the integration of differential privacy significantly reduced the risk of data breaches, providing robust protection against adversarial attacks. Conclusion: The FL model reported a performance difference which was only slightly reduced as compared with the centralized model. Moreover, this privacy compliance overcomes the risk of data breaches by integrating with differential privacy & securing against adversarial attacks.

In modern air defense systems, safeguarding sensitive information is crucial to prevent unauthorized access and cyber-attacks. Here, we present an innovative image encryption approach, leveraging chaotic logistic maps and hyperchaotic Folded Towel Map sequence generation. The proposed image encryption is a multi-layered procedure intended to secure image transmission. It initiates with permutation, where a chaotic logistic map generates pseudo-random sequences to scramble pixel positions. Next, key mixing creates complexity, randomness, and nonlinearity using an invertible key matrix. Finally, the diffusion phase employs hyperchaotic maps to produce a new sequence XORed with the pixels through a bitwise operation, further encrypting the image. This three-stage process efficiently protects images from unauthorized access, ensuring secure transmission. The proposed method enhances security by leveraging non-linearity, sensitivity, and robust mixing, properties making it highly resistant to cryptographic attacks. The experimental results showed robust encryption performance as established by metrics such as an entropy value of 7.9991, a UACI of 33.21%, and an NPCR of 99.61%. The proposed encryption approach outperformed existing methods in securing image transmission and storage, offering a reliable solution for protecting air defense communication strategic data.

Abstract Abstract 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.

n the rapidly evolving landscape of digital communication, ensuring data security remains a paramount concern. This research proposes a hybrid encryption technique that combines the strengths of Elliptic Curve Cryptography (ECC) and the Data Encryption Standard (DES) to enhance the security of data transmission. The hybrid model leverages the efficiency of DES for symmetric encryption and the robust security features of ECC for asymmetric encryption. The methodology involves first encrypting data using DES and subsequently applying ECC to the DES-encrypted output, thus providing a dual-layer encryption mechanism. The system was implemented using C# on Visual Studio and tested across five plaintext files ranging from 319 MB to 10,127 MB in size. The findings demonstrate that while the hybrid system slightly reduces throughput compared to using DES or ECC alone, it significantly enhances data security. Specifically, encryption throughput for DES was recorded at 64.53%, with ECC at 34.67%. Decryption throughput was 66.87% for DES and 67.86% for ECC, while the hybrid approach showed a balanced yet secure performance. The memory usage of the hybrid system, although higher, offers an acceptable trade-off for enhanced security, growing from 21.34 MB to 73.87 MB depending on the file size. The proposed hybrid approach provides a crucial balance between performance and security, offering a robust solution for secure data communication. It contributes significantly to the field by addressing the limitations of individual encryption algorithms, ensuring higher security at the cost of minimal performance reduction.

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]

This uses homomorphic encryption in cloud-based platforms to improve electronic health records (EHR) security and accessibility. Protecting sensitive medical data while enabling data processing and analysis is the main goal. The study examines how homomorphic encryption protects EHR data privacy and integrity. Its main purpose is to reduce risks of unauthorized access and data breaches to build trust between healthcare professionals and patients in digital healthcare. The research uses homomorphic encryption to safeguard cloud EHR storage and transmission. Results will highlight the algorithm's influence on data security and computing efficiency, revealing its potential use in healthcare to protect patient privacy and meet regulatory requirements. Results from dataset of patient health metrics show in the 1st instance sample data for 5 instances with ages between 57 to 88, blood pressure (BP) values from 33 to 85, glucose values from 5 to 99, and heart rate values from 24 to 88. In another study of 5 patients, cholesterol levels ranged from 10 to 80 mg/dL, body mass index (BMI) from 10 to 96 kg/m², smoking status from 14 to 79, and medication adherence from 6 to 78%. [ABSTRACT FROM AUTHOR]

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

Loan frauds in India have gotten more difficult by exploiting financial system vulnerabilities. Online purchasing has exacerbated these frauds. Identity fraud, phoney paperwork, and unclear loan conditions are common. This article looks at how blockchain and IoT could make loans safer, more open, and more efficient, reducing loan fraud. On an independent blockchain network, the proposed IoTBlockFin system records all loan events. This opens up the system and prevents dishonest alterations. IoT devices verify borrower identities and property, reducing false claims. An online loan application and smartphone app allow remote loan status checks. This speeds up and simplifies client service. Blockchain's digital safety measures protect sensitive user and transaction data from unauthorised parties. This prevents data breaches and illegal access. This comprehensive approach reduces loan frauds and improves financial transactions. IoTBlockFin seeks to solve today's lending process, which will transform India's banking business. [ABSTRACT FROM AUTHOR]

The Healthcare Internet of Things (HIoT) is driving a paradigm shift in the healthcare business by providing safe, fast, and networked healthcare solutions. We examined the advantages, disadvantages, and potential future of the Internet of Things (IoT) in the medical industry. Scalability, accuracy, real-time monitoring, data security, and interoperability were among the top priorities. The study employed strict assessment criteria to compare the proposed HIoT technology to existing approaches. This article begins with an overview of the IoT in healthcare. This study compares and contrasts the proposed HIoT strategy with more conventional approaches. We applied both methodologies in this study, each with its own benefits and drawbacks. We evaluated the responses using the F1-score, recall, accuracy, and precision. The inquiry uncovered an interesting story. The proposed HIoT method outperformed traditional techniques in all assessment parameters. In terms of accuracy, the recommended solution outperformed "Block chain Encryption" (8.4) and "Data Validation" (7.9). Additionally, it received an 8.9 for real-time monitoring and an 8.8 for interoperability. Another benefit of the strategy was a reduction in medical errors. The high data accuracy score of 9.1 demonstrates this. The findings illustrate the potential transformation of healthcare delivery through the Internet of Things. According to the study, the proposed strategy might increase healthcare's efficacy, efficiency, and patient-centeredness. The Internet of Things has opened up exciting new opportunities in healthcare. These options may transform medical care and patient outcomes. [ABSTRACT FROM AUTHOR]

Have you ever experienced the frustration of trying to hit a bullseye on a moving target? In the medical device industry, cybersecurity is one of the fastest-moving targets. Medical device [...]

I am going to wade into a subject not usually discussed in polite circles; in fact, it may be considered by some to be a 'third rail' topic. Specifically, I [...]

Abstract The modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.

This study investigated the level of awareness of personal data protection among Facebook** and Instagram** users. The research evaluated the number of Facebook and Instagram users who have taken precautions to secure their personal data, why they did so, and the identification of regulatory authorities responsible for data protection in Nigeria. The Privacy Calculus Theory was used as the theoretical framework. The survey research design was used for this study, with the web-based questionnaire as the instrument for data collection. A sample size of 396 was drawn from the student population at the University of Port Harcourt. The data were analyzed in tables using frequency and percentage. The results indicated that users are strongly aware of the significance of safeguarding personal data on Facebook and Instagram. The users acknowledged implementing precautionary steps to safeguard their personal data. However, they still need to determine the specific agencies and regulatory entities accountable for ensuring the safety of personal data. In order to tackle this problem, the study recommended that the University management and student bodies should organize specific educational workshops to promote understanding of the pertinence of safeguarding personal data on social media platforms (Solove, 2021), create easily accessible materials and guidelines on the University of Port Harcourt portal designed for social media users, outlining the best methods for protecting personal data, including personal data protection awareness in its orientation programs and student events and also set up a system for reporting potential data breaches and offer assistance to affected users. ** – A social network owned by “Meta”, which is recognized as extremist in Russia

With the development of cross-border telemedicine, international cooperation in clinical trials of drugs and vaccines, cross-border academic discussions and clinical research, cross-border flow of health and medical data has become an irreversible trend. Rules for cross-border flow of health data require doing a balance among the objectives and policies of maintaining national health security, protecting citizens' privacy, and promoting the development of health industry. This paper uses literature research, comparative analysis and other methods to study the legislative regulation and regulatory mechanism of cross-border health data in Australia. The study's findings reveal that Australia prioritizes health data security, enforcing stringent oversight on the cross-border movement of personally identifiable health data through dedicated agencies and comprehensive legislation. China should build a scientific, complete and strict legal and policy system for health data protection and cross-border flow, and organically combine and implement laws and regulations with policy guidelines; improve the terms of standard contract for health data exit and play the function of the standard contract for regulation; actively use the existing hierarchical classification of health data to build a flexible and diverse health data exit regulation model; and strengthen exchanges and cooperation with countries along the "Belt and Road", especially ASEAN member countries, in the field of cross-border health data flow.

With the rapid development of science and technology, machine learning has become a key factor driving the progress of enterprises. However, for small and medium-sized enterprises, the amount of data and computing power often become obstacles to apply machine learning. The rise of computing power networks has brought new opportunities for enterprises, accompanied by new challenges such as data security. A linear regression scheme for multi-user security collaborative computing based on computing power networks was proposed. The scheme allowed multiple users to achieve secure joint training in a computing power network using sensitive data to build a linear regression model. The scheme adopts low-cost blinding method and homomorphic encryption technology to encrypt user sensitive data to protect the security of sensitive data.

Abstract As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

Abstract Although chatbots are used a lot for customer relationship management (CRM), there needs to be more data security and privacy control strategies in chatbots, which has become a security concern for financial services institutions. Chatbots gain access to large amounts of vital company information and clients’ personal information, which makes them a target of security attacks. The loss of data stored in chatbots can cause major harm to companies and customers. In this study, STRIDE (viz. Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) modelling was applied to identify the data security vulnerabilities and threats that pertain to chatbots used in the insurance industry. To do this, we conducted a case study of a South African insurance organisation. The adopted methodology involved data collection from stakeholders in the insurance organisation to identify chatbot use cases and understand chatbot operations. After that, we conducted a STRIDE-based analysis of the chatbot use cases to elicit security threats and vulnerabilities in the insurance chatbots in the organisation. The results reveal that security vulnerabilities associated with Spoofing, Denial of Service, and Elevation of privilege are more relevant to insurance chatbots. The most security threats stem from Tampering, Elevation of privilege, and Spoofing. The study extends the discussion on chatbot security. It fosters an understanding of security threats and vulnerabilities that pertain to insurance chatbots, which is beneficial for security researchers and practitioners working on the security of chatbots and the insurance industry.

The widespread practice of Bring your own device (BYOD) to work significantly raises cybersecurity risks. All organization’s employees and employers will greatly benefit from this trend. The growth of spyware, malware and other dubious downloads on individual devices has compelled the government to review its data security guidelines. Without user knowledge, hazardous apps are downloaded to personal devices. Both people and governments could suffer tragic consequences as a result of this. In this situation, BYODs are problematic since they can alter policies without permission and release private information. The main goal of the research was to detect fraudulent communications coming from BYOD environments. A network monitoring and managing information configuration settings of mobile devices in the network is established by providing policies and authenticating endpoint devices in the BYOD network using a mix of NAC and MDM. This framework deals with Security Manager (SaaS) as the second module is briefly described. The study's early results were positive and suggested that the framework would lessen access control-related issues.

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