Improved security and privacy for online platforms. Online platforms provide goods and services to people all over the world in a flexible way. Due to COVID-19, the number of online platforms increased significantly. As more and more business activities are conducted in a virtual environment, there is a corresponding increase in major privacy and security challenges. This project aims to work in the online education industry to provide a revolutionary secure environment for both business owners ....Improved security and privacy for online platforms. Online platforms provide goods and services to people all over the world in a flexible way. Due to COVID-19, the number of online platforms increased significantly. As more and more business activities are conducted in a virtual environment, there is a corresponding increase in major privacy and security challenges. This project aims to work in the online education industry to provide a revolutionary secure environment for both business owners and users. This secure online environment will enable privacy and security guarantees that will be first implemented on our Partner Organisation’s education platform. The developed technologies can be easily adapted to most online-service industries and can be commercialised immediately.Read moreRead less
Deep Learning Attacks and Active Defences: A Cybersecurity Perspective. The belief that deep learning technology is imperative for economic development, military control, and strategic competitiveness has accelerated its development across the globe. However, experience has revealed the disappointing fact that deep learning models are vulnerable to a range of security attacks. Hence, a series of methodologies and defence strategies will be devised that make deep learning systems robust to these ....Deep Learning Attacks and Active Defences: A Cybersecurity Perspective. The belief that deep learning technology is imperative for economic development, military control, and strategic competitiveness has accelerated its development across the globe. However, experience has revealed the disappointing fact that deep learning models are vulnerable to a range of security attacks. Hence, a series of methodologies and defence strategies will be devised that make deep learning systems robust to these attacks. The methodologies require analysing attack lifecycles to identify them in their early stages. With this knowledge, active defence methods and forensic strategies can be developed to ensure efficient defences and prevent further attacks. Moreover, the outputs will be generalisable to most deep learning services.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100282
Funder
Australian Research Council
Funding Amount
$432,000.00
Summary
New Frontiers for Anonymous Authentication. The project aims to investigate the new concepts and constructions of anonymous authentication protocols, which can both fill existing research gap and address new challenges raised by new computing technologies. The expected outcomes are novel concepts and methods in constructing anonymous authentication protocols with enhanced functionalities and better efficiency. The project will contribute to safeguard cybersecurity for all Australians and provide ....New Frontiers for Anonymous Authentication. The project aims to investigate the new concepts and constructions of anonymous authentication protocols, which can both fill existing research gap and address new challenges raised by new computing technologies. The expected outcomes are novel concepts and methods in constructing anonymous authentication protocols with enhanced functionalities and better efficiency. The project will contribute to safeguard cybersecurity for all Australians and provide significant benefits, such as advancing theoretical knowledge in the research field and enhancing privacy and security of all Australian online services.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100001
Funder
Australian Research Council
Funding Amount
$424,064.00
Summary
Regulations in Privacy-Preserving Blockchain Systems. This project aims to develop an integrated regulatory paradigm for privacy-preserving blockchain. This project expects to reduce cybercrimes and illegal transactions in blockchain and provide solutions for the regulation concerns raised in the national blockchain roadmap, using interdisciplinary approaches and new primitives. Expected outcomes of this project include providing versatile regulation services covering the whole lifetime of trans ....Regulations in Privacy-Preserving Blockchain Systems. This project aims to develop an integrated regulatory paradigm for privacy-preserving blockchain. This project expects to reduce cybercrimes and illegal transactions in blockchain and provide solutions for the regulation concerns raised in the national blockchain roadmap, using interdisciplinary approaches and new primitives. Expected outcomes of this project include providing versatile regulation services covering the whole lifetime of transactions while maintaining transaction privacy and user anonymity. This should provide significant benefits to the economy by reducing the financial loss caused by blockchain abuse worldwide ($76 billion per year) and promoting Australia’s blockchain ecosystem (grow to AU$68.4 billion by 2030). Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101329
Funder
Australian Research Council
Funding Amount
$432,355.00
Summary
Trading Privacy, Bandwidth and Accuracy in Algorithmic Machine Learning. This project aims to investigate the trade-offs between privacy, communication costs and accuracy of results when learning from users' sensitive data. The project intends to design faster and more accurate algorithms for a wide range of machine learning tasks by developing a novel and widely-applicable algorithmic framework. Expected outcomes of this project include new theoretical tools to guide the design of data-driven d ....Trading Privacy, Bandwidth and Accuracy in Algorithmic Machine Learning. This project aims to investigate the trade-offs between privacy, communication costs and accuracy of results when learning from users' sensitive data. The project intends to design faster and more accurate algorithms for a wide range of machine learning tasks by developing a novel and widely-applicable algorithmic framework. Expected outcomes of this project include new theoretical tools to guide the design of data-driven decision systems and rigorously analyse their performance and privacy guarantees. Privacy of individuals' information in data analytics pipelines is a key societal concern. This project should lead to significant benefits by strengthening privacy in these pipelines while also improving accuracy and cost-efficiency.Read moreRead less
Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend agains ....Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend against cyberattacks for both organisations and individuals in Australia and beyond, theory development in graph theory, refined graph neural network models and improved graph transfer learning algorithms.Read moreRead less
Balance and reinforcement: privacy and fairness in high intelligence models. The aim of this project is to develop a series of privacy preservation methods to achieve a new balance between privacy and fairness in highly accurate intelligence models. The main issue in achieving this goal is that high-accuracy intelligence technologies have resulted in significant privacy violations and are very vulnerable to issues of unfairness. This project will analyse the privacy risks associated with intelli ....Balance and reinforcement: privacy and fairness in high intelligence models. The aim of this project is to develop a series of privacy preservation methods to achieve a new balance between privacy and fairness in highly accurate intelligence models. The main issue in achieving this goal is that high-accuracy intelligence technologies have resulted in significant privacy violations and are very vulnerable to issues of unfairness. This project will analyse the privacy risks associated with intelligent systems and devise mechanisms to mutually reinforce both privacy and fairness based on the theoretical foundations laid by our analysis. These outcomes will enable model owners to effectively protect their intellectual property and offer services to users in a private, fair, and accurate manner.Read moreRead less