Potential Breaches Research Articles

Artificial Intelligence: An Untapped Opportunity for Equity and Access in STEM Education

Artificial intelligence (AI) holds tremendous potential for promoting equity and access to science, technology, engineering, and mathematics (STEM) education, particularly for students with disabilities. This conceptual review explores how AI can address the barriers faced by this underrepresented group by enhancing accessibility and supporting STEM practices like critical thinking, inquiry, and problem solving, as evidenced by tools like adaptive learning platforms and intelligent tutors. Results show that AI can positively influence student engagement, achievement, and motivation in STEM subjects. By aligning AI tools with Universal Design for Learning (UDL) principles, this paper highlights how AI can personalize learning, improve accessibility, and close achievement gaps in STEM content areas. Furthermore, the natural intersection of STEM principles and standards with the AI4K12 guidelines justifies the logical need for AI–STEM integration. Ethical concerns, such as algorithmic bias (e.g., unequal representation in training datasets leading to unfair assessments) and data privacy risks (e.g., potential breaches of sensitive student data), require critical attention to ensure AI systems promote equity rather than exacerbate disparities. The findings suggest that while AI presents a promising avenue for creating inclusive STEM environments, further research conducted with intentionality is needed to refine AI tools and ensure they meet the diverse needs of students with disabilities to access STEM.

Implementing zero-knowledge proof authentication on Hyperledger fabric to enhance patient privacy and access control

In recent years, the healthcare sector has encountered significant challenges in authenticating identities for online medical services. A predominant reliance on centralized identity management systems (IDMs) has presented obstacles to the seamless exchange of patient identities among various healthcare institutions, often resulting in data isolation within individual silos. Of paramount concern are the potential privacy breaches associated with centralized IDMs, which may compromise patient confidentiality. In response to these challenges, we propose a novel approach to securely sharing patient details across multiple hospitals utilizing the zero-knowledge access protocol (MediCrypt-ZKAP) within the Hyperledger Fabric blockchain framework. By adopting MediCrypt-ZKAP, hospitals can effectively verify the identities of requesting entities without disclosing sensitive patient information, thereby ensuring the highest levels of confidentiality and privacy protection. The proposed system represents a proactive step towards addressing the critical need for secure and interoperable patient data exchange within the healthcare sector. Through the integration of MediCrypt-ZKAP into existing blockchain infrastructure, our solution aims to enhance data security and privacy while promoting seamless collaboration among healthcare institutions.

A dynamic method of assessing potential distribution network policy breaches due to localised load reduction flexibility service under varying levels of generation

A dynamic method of assessing potential distribution network policy breaches due to localised load reduction flexibility service under varying levels of generation

Integrated strategies for database protection: Leveraging anomaly detection and predictive modelling to prevent data breaches

The protection of database systems has become a critical priority in the digital era, where data breaches pose significant threats to organizational integrity, financial stability, and public trust. Traditional security measures, while essential, are increasingly insufficient to combat sophisticated cyber threats. This paper examines integrated strategies for database protection, focusing on the complementary roles of anomaly detection systems and predictive modelling in identifying and mitigating potential breaches. Anomaly detection systems leverage machine learning algorithms to monitor database activities in real time, flagging irregular patterns indicative of unauthorized access or unusual data usage. These systems enhance the speed and accuracy of threat detection, reducing the time between intrusion attempts and remediation. Predictive modelling complements this approach by analysing historical breach data to proactively identify vulnerabilities within database infrastructures. By combining real-time anomaly detection with predictive analytics, organizations can develop robust defense mechanisms against evolving cyber threats. The study highlights successful implementations of these integrated strategies through case studies in critical sectors such as finance, healthcare, and government. In these instances, the fusion of anomaly detection and predictive modelling significantly improved breach prevention and response times, mitigating potential data loss and reputational damage. This paper concludes by emphasizing the importance of adopting an integrated, data-driven approach to database security. By leveraging advanced analytics and real-time monitoring, organizations can not only protect sensitive information but also anticipate future threats, ensuring the resilience of their database systems in an increasingly hostile cyber environment.

Establishing A Personal Data Protection Agency for E-Commerce in Indonesia

The rapid growth of e-commerce in Indonesia has led to a significant increase in the collection and processing of personal data, raising concerns regarding data security and privacy rights. This study analyzes the urgency of establishing a Personal Data Protection Agency (LPDP) specifically for e-commerce users in Indonesia, considering the increasing risks to personal data in the digital marketplace. This research focuses on addressing the limitations of the current legal framework, particularly the gaps in the Indonesian Personal Data Protection Law (UU No. 27 of 2022), and proposes an independent body with clear authority to regulate, monitor, and enforce data protection standards. This study employs a qualitative approach using normative legal analysis to evaluate existing regulations and assess the evolving needs of the e-commerce sector. The findings suggest that the absence of detailed implementation regulations and lack of a specific regulatory body create significant legal uncertainties, exposing users to potential data breaches. Establishing the LPDP is expected to strengthen data protection measures, enhance consumer trust, and provide legal certainty in Indonesia's digital economy. The proposed structure of the LPDP includes directorates for policy and regulation, supervision and audits, law enforcement, and public education and awareness. The implementation of effective personal data protection policies requires a comprehensive and coordinated approach, with the LPDP having sufficient authority and resources to perform its duties. This study highlights the importance of establishing an independent regulatory body to ensure the protection of personal data and privacy rights in Indonesia's rapidly expanding e-commerce sector.

MITIGATING CYBER THREATS WITH ROBUST IDENTITY AND ACCESS MANAGEMENT TECHNIQUES

Abstract—Identity and Access Management (IAM) is a fundamental security framework that ensures legitimate users can access critical resources within a computer system while preventing unauthorized access. This is especially vital in financial organizations, where safeguarding sensitive information such as user identities, financial data, and medical records is paramount. These organizations face a heightened risk of cyberattacks due to the valuable and often high- stakes nature of the data they manage. Data breaches in such institutions can result in identity theft, billing fraud, insurance fraud, and other serious consequences, making robust IAM solutions essential. Traditional IAM systems rely on user credentials, roles, and policies to manage access, but as threats become more sophisticated, so must the defense mechanisms. Recent advances in machine learning offer promising methods for enhancing IAM systems to detect and prevent potential security breaches in real- time. One such approach is the use of Identity Markup- based IAM systems, which integrate user behavior and context-based attributes to assess the legitimacy of access requests. By embedding advanced algorithms, such as ensemble models, within these systems, organizations can better predict, identify, and mitigate threats before they escalate. This paper proposes an Identity Markup-based IAM solution enhanced by an Ensemble ExoBoost Tree for detecting and preventing security attacks within financial organizations. The ExoBoost tree, a variant of gradient boosting, combines multiple decision trees to provide a powerful, accurate method for detecting anomalies and potential attack patterns based on historical data and user behavior analysis. By incorporating this machine learning-driven approach, the IAM system not only controls access more effectively but also enhances its ability to identify subtle attack vectors, even those previously unknown. The proposed solution aims to address the growing complexity of cybersecurity challenges in financial organizations by providing a robust, adaptive IAM system capable of preemptively identifying and mitigating future data attacks. By leveraging advanced machine learning techniques, this approach offers a significant advancement over traditional IAM systems, ensuring the ongoing protection of sensitive financial data against emerging threats. Index Terms—Identity and Access Management,Identity Markup-based IAM,Ensemble ExoBoost Tree

Vulnerability Evaluation for Student Enrollment at SMKS Pandawa Bali Global Abiansemal

The development of information technology has driven the adoption of digital student enrollment systems by various educational institutions, including SMKS Pandawa Bali Global Abiansemal. The school employs the CodeIgniter 3 framework with standard configurations to enhance efficiency, transparency, and accessibility in the student admission process. However, this technological progress also introduces significant security risks, such as potential data breaches, system integrity disruptions, and damage to the school's reputation. This study aims to evaluate the security vulnerabilities of SMKS Pandawa Bali Global’s online registration system through risk analysis and penetration testing. The research methodology involves a literature review, identification of vulnerabilities, risk assessment, and formulation of recommendations. The findings reveal several critical vulnerabilities, including risks of SQL injection attacks, Cross-Site Scripting (XSS), and insecure session management. To address these issues, the study recommends implementing security measures such as deploying a Web Application Firewall (WAF), enabling multi-factor authentication, conducting regular security testing, and providing security training for staff. By adopting these measures, the school can enhance the security and stability of its enrollment system, ensuring a smoother, safer, and more reliable student registration process in the future.

Malaysian Public’s Perception Toward Event Data Recorder (EDR) in Vehicles

The Event Data Recorder (EDR) is an important device in a vehicle that can be used to analyze vehicle accidents. EDRs record and store crucial sensor data before, during, and after accidents, which can be used in reconstructing accident events. EDR has been regulated in the US and UK; however, its implementation in the ASEAN region, especially in Malaysia, is relatively new. In this study, a survey is conducted to investigate the perception of Malaysians toward EDR. There are three focuses of this survey: (1) the public's awareness of EDR's existence; (2) their perception of the benefits of EDR; and (3) their privacy concerns with the use of EDR in vehicles. The survey revealed that a majority (75.4%) of respondents were unaware of the existence of EDR, but 72.3% acknowledged that EDR could help identify accident causes, and 59.5% believed it could promote safer driving. Moreover, over 66% of respondents agreed that EDR could enhance vehicle and road safety. Besides, 40.3% expressed concern about potential privacy breaches and misuse of EDR data. Despite that, nearly 80% of respondents were in favor of installing EDR in their vehicles and allowing the data to be used in court. Additionally, 70% indicated that EDR would become a criterion for vehicle selection, and they would support mandatory EDR regulations.

A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity

In the 4th industrial era, the proliferation of interconnected smart devices and advancements in AI, particularly big data and machine learning, have integrated various industrial domains into cyberspace. This convergence brings novel security threats, making it essential to prevent known incidents and anticipate potential breaches. This study develops a scenario-based evaluation system to predict and evaluate possible security accidents using the MITRE ATT&CK framework. It analyzes various security incidents, leveraging attack strategies and techniques to create detailed security scenarios and profiling services. Key contributions include integrating security logs, quantifying incident likelihood, and establishing proactive threat management measures. The study also proposes automated security audits and legacy system integration to enhance security posture. Experimental results show the system’s efficacy in detecting and preventing threats, providing actionable insights and a structured approach to threat analysis and response. This research lays the foundation for advanced security prediction systems, ensuring robust defense mechanisms against emerging cyber threats.

Learn best practices to delete confidential data

The Family Educational Rights and Privacy Act does not require schools to destroy student records. To reduce technology costs and limit potential data breaches, many schools and districts establish their own retention policies, including timeframes for the eventual destruction of unnecessary records. But how can institutions know if their data destruction practices comply with their FERPA duty to protect personally identifiable information in a student record?

Enhancing Big Data Security in Hadoop using Machine Learning

In the era of Big Data, where vast amounts of information are generated and analysed to extract valuable insights, ensuring the security of data has become paramount. Hadoop, as a prominent framework for processing and analysing Big Data, presents unique challenges in terms of security due to its distributed and decentralized architecture. Traditional security mechanisms in Hadoop, such as authentication, authorization, and encryption, are essential but may not suffice to address evolving security threats effectively. This research paper proposes an innovative approach to enhance Big Data security in Hadoop using Machine Learning techniques. Machine Learning offers the capability to detect anomalies, identify patterns, and classify data, which can complement traditional security measures and provide proactive defence mechanisms against sophisticated attacks. The literature review highlights the limitations of existing security mechanisms in Hadoop and discusses the potential of Machine Learning in addressing these challenges. Various Machine learning algorithms, including anomaly detection, pattern recognition, and classification, are explored for their applicability in Big Data security. The proposed methodology involves integrating Machine Learning algorithms into the Hadoop ecosystem to analyse data access patterns, detect abnormal behaviour, and identify potential security breaches in real-time. The experimental setup comprises the selection of relevant datasets, implementation details using appropriate tools and frameworks, and evaluation using established metrics. Results from experiments demonstrate the effectiveness of the proposed approach in enhancing Big Data security in Hadoop. By leveraging Machine Learning, organizations can improve their ability to detect and mitigate security threats, thereby safeguarding sensitive data and preserving the integrity of their Big Data infrastructure. The discussion section interprets the findings in the context of existing literature, highlighting the significance of the research and identifying avenues for further exploration. Ultimately, this research contributes to the advancement of Big Data security practices by leveraging Machine Learning techniques to fortify the defences of Hadoop-based systems against evolving cyber threats.

Overview of AI regulation in healthcare: A comparative study of the EU and South Africa

This article provides a comparative analysis of the regulatory landscapes governing artificial intelligence (AI) in healthcare in the European Union (EU) and South Africa (SA). It critically examines the approaches, frameworks and mechanisms each jurisdiction employs to balance innovation with ethical considerations, patient safety, data privacy and accountability. The EU’s proactive stance, embodied by the AI Act, offers a structured and risk-based categorisation for AI applications, emphasising stringent guidelines for risk management, data governance and human oversight. In contrast, SA’s regulatory environment is characterised by its infancy and lack of specificity, with existing legislation such as the National Health Act and the Medicines and Related Substances Act providing a foundational but limited framework for addressing the unique challenges posed by AI in healthcare. The article delves into the dynamic nature of AI technologies, highlighting the need for continuous risk assessment, the importance of transparent and responsible data governance and the critical role of human oversight in ensuring patient safety and autonomy. It discusses the obligation of clear liability frameworks to address potential malfunctions and security breaches in AI applications. Through this comparative lens, the manuscript identifies regulatory gaps and proposes that the South African Law Reform Commission (SALRC) should play a predominant role in developing draft legislation for AI prior to the evolution of challenges related to these technologies.

Evaluation of CO2 Leakage Potential through Fault Instability in CO2 Geological Sequestration by Coupled THMC Modelling

A faulted saline aquifer system was simulated in a coupled thermal-hydraulic-mechanical-chemical (THMC) framework to examine the potential breaching of the caprock seal from long-term CO2 sequestration. The pH of the brines steadily dropped from 7.5 to 4.7 due to the continuous injection of scCO2 (supercritical CO2), which was caused by the rapid dissolution of calcite in the reservoir and associated fault zones, alongside alterations in the concentrations of primary and secondary minerals within the formation. The increase in pore pressure with the continuous injection of scCO2 triggered fault reactivation at 6y with the resultant leakage of CO2 along the fault. This builds CO2 saturation inside the fault at 7y to six-fold higher than pre-slip. Continuing shear reactivation and creation of reactive surface area following the initial CO2 leakage accelerates dissolution/precipitation reactions, in turn further increasing porosity and permeability of the reservoir and fault. In particular, the permeability and porosity in the fault zone were increased by only 2% and 6%, respectively – staunched by competitive feedbacks in dissolution countered by precipitation that are individually much larger. Comparison of mineral concentrations adjacent to the fault before-and-after instability revealed that the development of shear failure also promotes the transport of reactivated minerals into the fault zone. Among them, feldspar changes most significantly in later stages, dominated by dissolution with the volume fraction decreasing by 80% and increasing the aqueous concentration of K+ by approximately an order of magnitude. However, secondary minerals counter this dissolution through precipitation with the volume fraction of kaolinite increasing by an order of magnitude compared with the original fraction. Finally, the evolution of the fault sealing coefficient (FS) demonstrates that the porosity and permeability exert a pivotal influence in controlling the self-sealing behavior in the basal of the fault, while the upper fault layer exhibits self-enhancing response. A notable observation is that changes in mineral ion concentrations in fault zones could be applied as a significant diagnostic signal to monitor fault stability and the potential for progress of self-sealing behavior.

Anomaly Detection System using Machine Learning Algorithms

The "Anomaly Detections Systems" is a advanced web application created to upgrade framework security and execution observing through machine learning strategies. This application, highlighting a strong login and dashboard interface for clients, empowers real-time location and examination of peculiarities inside complex frameworks. By leveraging progressed machine learning calculations, the framework can distinguish abnormal designs and behaviors that go astray from built up standards, subsequently giving early notices of potential issues or breaches. The user-friendly dashboard offers comprehensive bits of knowledge and visualizations, permitting directors to screen framework wellbeing and execution proficiently. Clients can get to point by point reports and real-time alarms, encouraging incite examination and reaction to recognized peculiarities. This proactive approach to framework observing not as it were improving security by recognizing potential dangers some time recently they heighten but moreover makes strides generally framework unwavering quality and execution. The integration of machine learning guarantees nonstop change and adjustment to advancing dangers and operational conditions. This venture underscores the basic part of innovation in keeping up vigorous and secure frameworks, exhibiting the potential of machine learning in proactive framework administration and irregularity location

AI-driven anomaly detection in cloud computing environments

The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.

GDPR Compliance of Hospital Management Systems in the UAE

While the UAE is making strides in healthcare digitalization and adopting global best practices, the absence of a unified data protection framework equivalent to the GDPR poses significant challenges for hospital management systems (HMS) in the region. This gap creates uncertainties in compliance, especially regarding cross-border data transfers, third-party vendor management, and the protection of patients' privacy rights. The lack of clear regulations tailored to the UAE’s unique healthcare landscape hinders the implementation of robust data protection measures, raising concerns about potential data breaches, legal liabilities, and the overall trustworthiness of healthcare institutions. Addressing these challenges is crucial for aligning the UAE’s healthcare sector with international standards while ensuring the security and privacy of patient data in a rapidly evolving digital environment. The General Data Protection Regulation (GDPR) has significantly impacted hospital management systems (HMS) by setting strict data protection requirements. This study provides a systematic literature review of GDPR compliance in HMS, focusing on key challenges such as regulatory complexity, permission management, data subject rights, data breaches, third-party vendor management, and cross-border data transfers. Suggested mitigation measures include privacy by design, data protection impact assessments, improved consent management, robust breach detection, and efficient vendor management. Legislative reforms are needed to clarify GDPR's application to healthcare. The study also highlights increased investments in privacy technologies, improved patient trust, and the demand for advanced solutions. Future research should explore the effectiveness of these mitigations, GDPR's impact on patient satisfaction, ethical data processing, and standardized data protection frameworks in healthcare. Achieving GDPR compliance is crucial for protecting patient data, building trust, and ensuring secure and ethical use of healthcare information. This study aims to guide healthcare organizations, particularly hospitals, along with regulators and researchers, in navigating these challenges and implementing effective solutions. Received: 17 June 2024 | Revised: 10 September 2024 | Accepted: 13 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Inas Al Khatib: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Visualization, Project administration. Norhan Ahmed: Methodology, Formal analysis, Writing - original draft, Supervision, Funding acquisition, Project administration. Malick Ndyiaye: Writing - review & editing, Supervision.

You Are What You Write: Author re-identification privacy attacks in the era of pre-trained language models

The widespread use of pre-trained language models has revolutionised knowledge transfer in natural language processing tasks. However, there is a concern regarding potential breaches of user trust due to the risk of re-identification attacks, where malicious users could extract Personally Identifiable Information (PII) from other datasets. To assess the extent of extractable personal information on popular pre-trained models, we conduct the first wide coverage evaluation and comparison of state-of-the-art privacy-preserving algorithms on a large multi-lingual dataset for sentiment analysis annotated with demographic information (including location, age, and gender). Our results suggest a link between model complexity, pre-training data volume, and the efficacy of privacy-preserving embeddings. We found that privacy-preserving methods demonstrate greater effectiveness when applied to larger and more complex models, with improvements exceeding >20% over non-private baselines. Additionally, we observe that local differential privacy imposes serious performance penalties of ≈20% in our test setting, which can be mitigated using hybrid or metric-DP techniques.

Swine Industry in Canada: Biosecurity in Live Animal, Semen Transportation, and Embryo Transfer

The pork industry, a key player in the Canadian economy, significantly contributes to livestock production, international trade markets, and employment. The multi-faceted swine industry is comprised of nucleus, multiplier, and commercial herds. The recognition and application of exemplary internal and external biosecurity measures are paramount for maintaining sustainable swine production. Historically, live animal transport has been an important means of disseminating superior genetics between production herds and poses a considerable risk for disease transmission and biosecurity breach points. The industry has evolved over time to employ other methods, through the shipping of boar semen and the adoption of artificial insemination practices to acquire genetic resources with a lower risk of disease transmission. These effective changes have led to an improvement in herd health, production performance, and efficiency. In particular, the industry’s dedication to enhanced biosecurity is evident in the effort to streamline embryo collection and transfer procedures. Such advances reduce the potential need to transport live animals, thereby lowering the risk of introducing infectious agents. This review article explores the different forms of disseminating swine genetics, namely, live animal, semen transportation, and embryo transfer, examining potential breaches in biosecurity, and discussing mitigation strategies that reduce disease transmission to protect the health of animal stock.

Expression of Concern.

The Editor in Chief and the Publisher are issuing an expression of concern to alert readers that the following article is under investigation due to a potential ethical breach regarding lack of transparency in the transplantation procedure as outlined in the article published by Rogers W et al (DOI: 10.1136/bmjopen-2018-024473): - Chen J, Zhong L. Clinical significance of serum hepcidin-25 levels in predicting invasive fungal disease in patients after transplantation. Eur Rev Med Pharmacol Sci 2013; 17 (13): 1769-1773-PMID: 23852902. Further updates will be provided once the investigation is completed. The authors have been notified about the ongoing investigation and the publication of this expression of concern.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.