Protection Of Sensitive Data Research Articles

Wearable sensors for cardiological monitoring in the SUS: regulatory challenges and impacts on Brazilian Public Health

Cardiovascular diseases are the leading cause of death in Brazil, with approximately 400,000 deaths annually, which corresponds to almost 30% of all deaths in the country. In addition to the impact on mortality, these conditions generate an enormous economic burden for the health system, with costs associated with the treatment of diseases such as heart attack and heart failure exceeding R$50 billion per year. In this context, the use of wearable devices for continuous monitoring of cardiovascular parameters emerges as an opportunity to improve the management and prevention of these diseases. This study conducts a scoping review of the regulatory challenges involved in the incorporation of wearables into the Unified Health System (SUS), with a focus on cardiological monitoring. Articles published between 2019 and 2024 were analyzed, discussing issues such as data accuracy, interoperability between systems, protection of sensitive data, and training of health professionals. In addition, the study examines the economic and social implications of the adoption of these technologies in a context of profound socioeconomic inequalities. The results indicate that, although wearables have the potential to transform cardiovascular care in Brazil, regulatory and technical barriers still limit their large-scale adoption in the SUS. Adequate regulation, aligned with the General Data Protection Law (LGPD), and investments in infrastructure and training are essential for the safe and effective integration of these technologies into public health.

Importance of Cybersecurity/The Relevance of Cybersecurity to Diabetes Devices: An Update from Diabetes Technology Society.

As medical devices become more integrated with wireless technologies, the risks of cyberattacks and data breaches increase, making stringent cybersecurity measures essential. The implementation of rigorous cybersecurity standards is essential for enhancing the cybersecurity of devices. This article examines the evolving cyber threats faced by the medical technology industry, the role of IEEE 2621 in providing comprehensive security benchmarks for medical devices, and the need for continuous risk assessments and adherence to regulatory standards to mitigate future cyber risks. Adherence to cybersecurity standards establishes ensures the effective protection of sensitive data and critical infrastructure.

Simulation-based evaluation of advanced threat detection and response in financial industry networks using zero trust and blockchain technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

Cybersecurity in Learning Systems: Data protection and privacy in educational information systems and digital learning environments

This research addresses the growing cybersecurity challenges within educational information systems and digital learning platforms, focusing on the protection of sensitive data and user privacy. The objective is to identify prevalent cybersecurity threats in these environments and propose effective solutions to mitigate them. A mixed-method approach is employed, combining a comprehensive literature review with a survey distributed to IT professionals and educators working in digital learning environments. The findings highlight the increasing sophistication of cyberattacks, including data breaches, phishing, and malware, which compromise the integrity and security of educational data. Moreover, the study reveals a gap in the implementation of robust cybersecurity policies, especially in underfunded educational institutions. The proposed solutions emphasize the integration of advanced encryption methods, multifactor authentication, and regular cybersecurity training for all stakeholders. In conclusion, this research underscores the importance of developing a resilient cybersecurity framework tailored to educational systems, ensuring the protection of both institutional data and the privacy of users, thereby enhancing trust and security in digital learning environments.

Protecting Infinite Data Streams from Wearable Devices with Local Differential Privacy Techniques

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams.

Large-scale agile security practices in software engineering

Purpose Security in large-scale agile is a crucial aspect that should be carefully addressed to ensure the protection of sensitive data, systems and user privacy. This study aims to identify and characterize the security practices that can be applied in managing large-scale agile projects. Design/methodology/approach A qualitative study is carried out through 18 interviews with 6 software development companies based in Portugal. Professionals who play the roles of Product Owner, Scrum Master and Scrum Member were interviewed. A thematic analysis was applied to identify deductive and inductive security practices. Findings The findings identified a total of 15 security practices, of which 8 are deductive themes and 7 are inductive. Most common security practices in large-scale agile include penetration testing, sensitive data management, automated testing, threat modeling and the implementation of a DevSecOps approach. Originality/value The results of this study extend the knowledge about large-scale security practices and offer relevant practical contributions for organizations that are migrating to large-scale agile environments. By incorporating security practices at every stage of the agile development lifecycle and fostering a security-conscious culture, organizations can effectively address security challenges in large-scale agile environments.

Secured mutual wireless communication using real and imaginary-valued artificial neuronal synchronization and attack detection

This research presents a cutting-edge security framework that integrates optimal Intrusion Detection System (IDS) and Artificial Neural Networks (ANNs)-based key exchange methods to enhance the reliability of mutual wireless communication in Internet of Medical Things (IoMT) networks. The advent of IoMT technology has brought about a significant transformation in patient care and healthcare operations. It allows for real-time monitoring and diagnosis to be conducted remotely. However, the protection of sensitive medical data has become a crucial issue that has to be addressed to maintain privacy and security. IoMT devices, which have limited processing capabilities, are especially susceptible to cyberattacks, therefore requiring the implementation of new security measures. The proposed methodology offers numerous advantages. By utilizing patient sensor data and analyzing network traffic, the suggested solution surpasses current methods in identifying and preventing network threats with increased precision. This research showcases the better effectiveness of Deep Learning (DL) models in identifying intrusions in IoMT settings, by examining 17 Machine Learning (ML) and 6 DL models. The suggested methodology relies on using a neural sequence of ANNs that consist of both real and imaginary-valued components. This approach enables reciprocal learning and synchronization, which in turn facilitates safe key distribution among IoMT devices. This novel method not only guarantees strong key exchange mechanisms but also enhances the overall security of IoMT networks. This paper presents a complete solution to solve the difficulties of protecting wireless communication in IoMT contexts. This approach performs better than comparable methods in the literature.

Provenance Information for Biomedical Data and Workflows: Scoping Review.

The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. This scoping review followed the methodological framework developed by Arksey and O'Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. RR2-10.2196/31750.

Communication-Efficient Wireless Traffic Prediction with Federated Learning

Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression scheme and an adaptive aggregation strategy for wireless traffic prediction. FedCE achieves gradient compression through top-K sparsification and can largely relieve the communication burdens between local clients and the central server, making it communication-efficient. An adaptive aggregation strategy is designed by quantifying the different contributions of local models to the global model, making FedCE aware of spatial dependencies among various local clients. We validate the effectiveness of FedCE on two real-world datasets. The results demonstrate that FedCE can improve prediction accuracy by approximately 27% with only 20% of communications in the baseline method.

Assessing the Effectiveness of Cyber Domain Controls When Conducting Cybersecurity Audits: Insights from Higher Education Institutions in Canada

This study validates a comprehensive cybersecurity audit model through empirical analysis in three higher education institutions in Canada. The research aims to enhance cybersecurity resilience by assessing the effectiveness of cybersecurity controls across diverse educational environments. Given the increasing frequency and sophistication of cyberattacks targeting educational institutions, this research is essential to ensure the protection of sensitive academic and personal data. Data were collected through detailed audits involving system vulnerabilities, compliance with security policies, and incident response management at each institution. The findings underscore the importance of tailored cybersecurity strategies and continuous auditing to mitigate cyber risks in the Canadian higher education sector. This study contributes to the field by validating a versatile audit tool that can be adapted to various institutional contexts, promoting enhanced cybersecurity practices and evaluating the effectiveness of cybersecurity safeguards across the higher education sector in Canada. The results of the audit model validations provide the cybersecurity maturity rating of each institution. Further research is recommended to refine the model and explore its application in other industries and sectors.

An efficient anonymous authentication scheme for blockchain assisted and fog-enabled smart grid

The growing integration of blockchain and fog computing with Smart Grid (SG) technologies highlights the need for strong security protocols, dependable communication channels, and sensitive user data protection. In order to meet the unique needs of SG, this research paper presents a novel and effective anonymous authentication scheme enhanced by fog computing and blockchain. The suggested scheme makes use of blockchain's decentralized structure to strengthen security protocols and ensure user privacy and anonymity within the SG ecosystem. Through the combination of fog computing's low latency benefits and blockchain's immutability, the suggested scheme creates a robust framework for secure authentication. The suggested approach gives considerable weight to reducing computational overhead and optimizing user identity privacy. Through comprehensive analysis, the study highlights the scheme's efficiency concerning authentication speed, resource utilization, and resilience against potential attacks. These results offer an important remedy for the ever-changing landscape of energy management systems and substantially advance the development of safe and privacy-preserving SG architectures.

Data-Driven Framework for Cloud Storage Security Optimization: Leveraging Predictive Analytics and Machine Learning to Enhance Threat Detection and Incident Response

As cloud storage adoption accelerates, securing sensitive data against evolving threats, including Advanced Persistent Threats (APTs), Zero-Day Exploits, and Insider Threats, has become paramount. This research introduces a data-driven framework that harnesses predictive analytics, machine learning (ML), and deep learning (DL) techniques to fortify threat detection and incident response in cloud storage environments. By integrating real-time monitoring via Security Information and Event Management (SIEM) systems, anomaly detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and predictive modeling through Graph-Based Anomaly Detection (GBAD), our framework identifies potential security risks and optimizes countermeasures. Leveraging ML algorithms, such as Random Forest and Support Vector Machines (SVM), our approach analyzes historical incident data, user behavior, and system logs to predict and prevent attacks. Key benefits include proactive security measures, reduced response times via Security Orchestration, Automation, and Response (SOAR), and minimized data breaches through Containerization (Docker) and Serverless Computing (AWS Lambda). This research advances the development of intelligent cloud storage security solutions, ensuring robust protection for sensitive data in cloud-based infrastructure, compliant with PCI-DSS, HIPAA, GDPR, NIST Cybersecurity Framework, and ISO 27001.

Data Privacy Vis- A- Vis the Digital Personal Data Protection Act, 2023

The right to privacy is an essential human entitlement that involves an individual's independence and authority over their personal data. In an era dominated by digital interactions and data-driven technologies, the right to privacy has become increasingly pertinent. The concept of data privacy encompasses the protection of personal information from unauthorized access, use, and disclosure. With the proliferation of online platforms and digital services, individuals are generating vast amounts of data, raising concerns about its misuse and exploitation. Data privacy in India has become a significant issue with the increasing digitization of services and the proliferation of personal data collection by both government and private entities. In 2023, the digital data protection act was enacted to specifically deal with Data privacy as well as Data protection. This paper attempts to analyse the right to privacy vis-à-vis the digital data protection act, 2023 and this analysis explores how these regulatory provisions address the emerging challenges to the protection of sensitive personal data.

Pengaruh Penggunaan Sistem Informasi Manajemen Berbasis Digital Terhadap Efisiensi Administrasi Pendidikan

This research aims to explore the concepts, characteristics and impact of using digital-based Management Information Systems (SIM) on the efficiency of educational administration. The method applied is library research or literature study, where researchers collect information from sources such as books and documents that are relevant to the research focus. The information collected is then analyzed qualitatively. Research findings show that the use of digital-based information systems has a significant influence on the efficiency of educational administration. This system enables the integration of information technology, automation of administrative tasks, and integrated management of data from various sources, which ultimately supports better decision making. SIM also comes with strong security policies and sensitive data protection mechanisms. The use of digital SIMs provides benefits such as increased data accessibility, reduced human error, and increased data accuracy. Educational institutions can manage resources more efficiently, improve communication and collaboration, and track performance in a more timely manner. Factors that influence the use of digital SIMs include technological infrastructure readiness, human resource capabilities, management support, and security policies.

Security Ontology in a Virtual University

Abstract In this paper we propose a cybersecurity ontology model designed for universities, aiming to facilitate the management and protection of sensitive data and information within the context of the growing cybersecurity threats. The proposed ontology includes four distinct hierarchical levels: the basic level, the conceptual level, the instance level and the relationships level. At the basic level, it defines essential terms and principles of cybersecurity, including concepts like vulnerability, threat, cyber-attack, security policies and security rules. At the conceptual level, the ontology categorizes information and cybersecurity systems, embracing domains such as data protection, authentication, authorization, and auditing. At the instance level, the ontology describes specific examples of information and cybersecurity systems used in universities, such as the library management system or the accounting management system. At the relationships level, the ontology establishes links between different categories of information and cybersecurity systems, as well as between these systems and the entities that use them, such as students, professors and administrative staff. By implementing this cybersecurity ontology, universities can improve the management and protection of their sensitive data and information, as well as respond more efficiently to cybersecurity threats.

Understanding the legal framework in Research Data Management: a study of selected academic libraries in Kenya

Rationale of Study – The study focused on the legal framework instrumental in protecting research participants' rights and interests. It also sought to identify potential legal risks and recommend strategies to mitigate them, promote ethical conduct, and enhance security for data reuse and sharing.Methodology – The study used a quantitative research design to gather quantifiable data. The target population was academic librarians and executive library users from Kenyatta and Egerton University. A total of 35 participants participated in the study. Structured open-ended questionnaires were used to obtain responses from participants purposively selected.Findings – The study revealed that academic institutions have policies. However, to maintain a healthy research ecosystem that facilitates accessibility, interdisciplinary collaboration, knowledge transmission, and data sharing, academic libraries need to implement research data management policies that match open science standards, such as promoting reuse in the context of adherence to intellectual property rights and security of sensitive data.Implications – The study sheds light on issues and legal concerns addressed or should be addressed by research data management policies adoption or being prepared for adoption in academic libraries. Its issues are intellectual property, protection of data against security breaches, terms and conditions regarding data use, and protection of sensitive data confidentiality.Originality – The paper shed light on research data management policy, precisely the issues which should be included in the policy, as well as legal concerns.

The Future of Patient Data Security: Exploring Emerging Technologies and Collaborative Approaches

Abstract: The protection of sensitive patient data has become a paramount concern in the increasingly digital landscape of the healthcare industry. As healthcare organizations adopt electronic health records and face a growing array of cyber threats, they must navigate a complex regulatory environment, including the Health Insurance Portability and Accountability Act (HIPAA), while implementing robust strategies to safeguard personal health information (PHI). This article provides a comprehensive overview of the challenges and best practices in healthcare data protection, examining the current regulatory framework, the various threats to patient data—such as cyber-attacks, insider threats, and human error—and the key strategies and technologies for enhancing security, including encryption, access controls, and employee training. The article also explores the potential of emerging technologies, such as block chain and artificial intelligence, in revolutionizing healthcare data management and security, and emphasizes the importance of collaboration among stakeholders, including healthcare providers, technology vendors, policymakers, and patients. By recapping the main challenges and strategies discussed, offering insights into the future of data protection in healthcare, and issuing a call to action for prioritizing patient data security, this article serves as a valuable resource for healthcare organizations seeking to strengthen their data protection measures and maintain patient trust in the digital age.

A mobile edge computing-focused transferable sensitive data identification method based on product quantization

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

An Analysis on Security Issues and Research Challenges in Cloud Computing

Cloud computing has completely transformed how businesses handle, store, and process data and applications. However, using cloud services brings several security issues that need to be resolved to guarantee the availability, confidentiality, and integrity of critical data. This abstract overviews vital aspects of cloud computing security and highlights emerging trends and research directions. Essential challenges of cloud computing security include ensuring data privacy and confidentiality, maintaining data integrity and trustworthiness, and addressing compliance with regulatory requirements. Identity and access management (IAM) remains a critical area, focusing on enhancing authentication mechanisms and access controls to mitigate the risks of unauthorized access and insider threats. Additionally, active research areas include securing cloud orchestration and management platforms, resilience and availability of cloud services, and addressing the unique security considerations of cloud native technologies. Interdisciplinary collaboration between researchers, industry practitioners, and policymakers is essential to develop innovative security solutions and best practices for cloud computing environments. By addressing these challenges and advancing the state of the art in cloud security, organizations can leverage the benefits of cloud computing while mitigating associated risks and ensuring the protection of sensitive data and resources. In summary, this review paper provides a holistic understanding of cloud computing security, offering insights into current practices, challenges, and future directions for ensuring the confidentiality, integrity, and availability of cloud based systems and data.

Guardians of the iot realm: a comparative analysis of cryptographic security solutions for bolstering iot device networks

The "Internet of Things" (IoT) is an emerging technology that allows electronic devices and sensors to connect with one another over the Internet. The IoT is a network of smart devices such as sensors that are connected through cables or wirelessly. Today, IoT devices are commonly used in medical science, industrial automation, and smart home automation, among other applications. Easy accessibility and the open-source environment of IoT devices are major threats to privacy and security. In this paper, we comprehensively discuss areas of IoT, including architecture, details of IoT layers with respect to security algorithms, protocols, attacks, and their mitigation. The primary purpose of this research is to provide an efficient and cryptographically proven algorithm scheme with appropriate hardware to improve IoT device network security. The presented research enhances the network security of IoT devices by presenting an algorithmic framework fortified by cryptography and supported by the finest hardware options. After evaluating several cryptographic algorithms and hardware options, a more secure solution for IoT infrastructure is proposed. The article in particular, proposes a detailed strategy that addresses security at every level of the IoT system. This research is an important step towards ensuring the integrity and security of IoT devices in a networked environment, with the primary goal of increasing the protection of sensitive data and interactions.