In today's digital world, our reliance on the internet is pervasive, influencing nearly every aspect of daily life, from morning routines to evening relaxation. Cybersecurity, though essential, often goes unnoticed, as users routinely send, fetch, and transfer files with little attention to security. While more secure alternatives are available, many individuals and organizations continue to use File Transfer Protocol (FTP) in 2024 due to its simplicity and accessibility. However, the widespread use of FTP, despite its vulnerabilities, calls for immediate attention and improved security measures. This paper introduces a hybrid model designed to enhance the security of FTP by detecting potential attacks effectively. Our approach begins with a comprehensive categorization of possible FTP-based attacks, divided into two types: pre-compromise attacks aimed at gaining unauthorized access, and post-compromise attacks that exploit an already compromised server. The model incorporates two parallel components: a rule-based system that flags known attack signatures promptly and a machine learning component trained on FTP log data to recognize sophisticated and emerging attack patterns. Our model not only detects and classifies attack types but also provides valuable insights into the attacker, the attack path, and specific methods used, offering a comprehensive approach to FTP security.
Though it is possible to use pure OpenMP or pure MPI to develop parallel and distributed memory applications respectively, both are often combined in a hybrid MPI/OpenMP model to target clusters of multi-core nodes. In this paper, we introduce and discuss the design and implementation of a source-to-source compiler, translating OpenMP-annotated source code to MPI/OpenMP. We evaluate the performance of the translated programs on the HPC Spartan cluster at the University of Melbourne.
In 2018, the European Union's General Data Protection Regulation (GDPR) was enacted to regulate and harmonise data collection and processing practices, protect individuals' rights, and enforce legal accountability and penalties on non-compliant organisations. The GDPR is widely regarded as the world's most stringent privacy and security law, with implications for future amendments and privacy laws in countries such as Australia. Although organisations have implemented various approaches and processes to meet GDPR requirements, they continue to face challenges in aligning their systems and processes to ensure full GDPR compliance. In this paper, we review existing technical approaches and analyse the extent to which they are GDPR compliant and meet the rights of data subjects. Except for consent management, the existing literature sparsely covers other rights. Additionally, existing technical approaches assume that the data controller is trustworthy and will act according to the consent granted after receiving personal data. If this assumption is relaxed, none of the rights can be ensured in the current technical approaches. Next, we present a conceptual design for a holistic framework that ensures almost all data subjects' rights in GDPR, even in untrustworthy environments. Rather than providing personal data to the data controller, the proposed framework manages consent and provides a secure environment to process data using consented or trusted algorithms, returning only the processed data to the data controller. The framework builds on and integrates existing technologies, namely blockchains, personal data stores, and trusted execution environments, to create a secure environment with transparent processing. It aims to address the gaps identified in current technical approaches, ensuring that data subjects' rights are preserved in compliance with GDPR, even in untrustworthy environments.
Recent progress in generative artificial intelligence (GenAI) has yielded significant advancements in healthcare, affecting radiology, medical imaging, drug development, patient diagnostics, and supply chain optimisation. These innovations promise more improved diagnoses and time-saving cost-effectiveness. However, GenAI's rapid implementation poses significant challenges for meeting regulatory, ethical, and trustworthiness standards. These challenges include data privacy issues, reproducibility concerns, algorithmic bias in training data causing disparities in outcomes, and a lack of transparency and explainability. Unresolved, these issues could negatively affect the public's confidence in and perception of GenAI systems. Addressing these challenges, international AI governance frameworks, including the EU AI Act and WHO guidelines, prioritize regulatory adherence, trustworthiness, and the explainability of healthcare AI systems. While such frameworks have expanded, a deficiency remains in translating policy into effective compliance mechanisms. We propose a Compliance Agentic Model (CAM) framework to help organizations comply with GenAI and machine learning (ML)-based solutions. The CAM framework establishes trustworthiness in GenAI applications used in healthcare, ensuring alignment with organizational values and ethical standards to enhance accountability and regulatory adherence.
This paper introduces a novel method for generating differentially private synthetic datasets that harnesses Bayesian networks to ensure the preservation of essential statistical properties and referential integrity across linked tables. To address the dual challenges of maintaining privacy and minimizing computational overhead, we introduce a decomposition scheme for additive Laplacian noise that significantly reduces computational costs while enhancing the efficiency of the differential privacy framework. Our methodology offers a robust solution for creating synthetic datasets that not only mimic the statistical characteristics of original datasets, but also safeguard sensitive information against inference attacks. Through comprehensive evaluations, we demonstrate the practicality and effectiveness of our approach, which achieves a significant speedup in noise injection, thereby facilitating real-time data analysis. This breakthrough contributes to the broader accessibility of complex data analysis, particularly benefiting sectors dealing with sensitive information by improving data privacy and security measures. Our findings represent a significant advancement in statistical methodologies and software, underscoring the ongoing necessity for innovation in data processing techniques.
