Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving ....Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving this problem. It proposes to develop a set of effective methods for privacy-preserving data publication through combining randomisation with anonymisation, and for classifying the published data through uncertainty leveraging by probabilistic reasoning and accuracy lifting by inter-flow correlation analysis and active learning.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101577
Funder
Australian Research Council
Funding Amount
$427,116.00
Summary
Microarchitectural attacks and JavaScript: threats and defences. This project aims to improve cybersecurity by identifying and mitigating vulnerabilities in Internet-connected computers. Expected outcomes of this project include novel techniques for protecting web browsers and cloud server, to prevent them from inadvertent leaks of private or sensitive information. This should provide significant benefits, such as reduced risk of cyberattacks and improved privacy for web users.
Intelligent Technologies for Smart Cryptography. This project aims to improve cybersecurity by automating the process of generating cryptographic software for smart devices. The expected outcomes are tools that automatically produce efficient cryptographic software that resists attacks. The main benefit of this project is to reduce the amount of expert labour required when developing secure software.