Discovery Early Career Researcher Award - Grant ID: DE220100595
Funder
Australian Research Council
Funding Amount
$416,400.00
Summary
Efficient privacy-preserving proofs for secure e-government and e-voting. Electronic systems are becoming increasingly widespread and crucial to social and economic wellbeing. This project aims to ensure that e-government, e-health, e-commerce and e-voting are secure and trustworthy by inventing new ways to verify these systems without infringing privacy. This project expects to use innovative techniques from cryptography to support development of trustworthy systems. Expected outcomes of this p ....Efficient privacy-preserving proofs for secure e-government and e-voting. Electronic systems are becoming increasingly widespread and crucial to social and economic wellbeing. This project aims to ensure that e-government, e-health, e-commerce and e-voting are secure and trustworthy by inventing new ways to verify these systems without infringing privacy. This project expects to use innovative techniques from cryptography to support development of trustworthy systems. Expected outcomes of this project include better support for organisations to build trustworthy systems that will maximise benefit to Australian business and society. This should provide significant commercial, reputational, and societal benefits by avoiding disruptions to the organisations and their clients if and when they are attacked. Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical application ....Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical applications such as better wellness tracking and lifestyle-related illness prevention, which will be particularly critical to Australia's aging population. This project will also serve as a vehicle to educate and train Australia’s young scholars and engineers.Read moreRead less
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
Innovations in Demographic Modelling for Government Analysis and Planning. This project aims to create innovative and cutting-edge demographic models to better meet the needs of practitioners and researchers. Together with the partner organisations, Commonwealth Treasury and the Australian Bureau of Statistics, it will focus on creating more accurate and fit-for-purpose forecasting methods for Australian fertility, mortality, and migration, including a policy scenario model to produce population ....Innovations in Demographic Modelling for Government Analysis and Planning. This project aims to create innovative and cutting-edge demographic models to better meet the needs of practitioners and researchers. Together with the partner organisations, Commonwealth Treasury and the Australian Bureau of Statistics, it will focus on creating more accurate and fit-for-purpose forecasting methods for Australian fertility, mortality, and migration, including a policy scenario model to produce population projections by visa/citizenship category and Australians overseas. Expected outcomes of this project include improved forecasting methods reported in open-access papers, user-friendly forecasting software and tools for the partner organisations, and a stronger relationship between researchers and practitioners.Read moreRead less
AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration vide ....AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration videos, new ways to represent, learn, and search for generalised policies that are scalable and robust, and approaches to verify and explain generalised policies. The new systems should benefit the aerospace industry by assisting humans in assembling and delivering aerospace products.Read moreRead less
New methods for drug discovery by NMR spectroscopy. This project aims to advance nuclear magnetic resonance (NMR) spectroscopy methods in the field of drug discovery. It addresses a long-standing bottleneck for medicinal chemists in drug development: the rapid determination of how ligand molecules bind to proteins, where they bind and their orientation in the binding site. The methods include techniques for the attachment of NMR tags to ligands and target proteins, installation of new unnatural ....New methods for drug discovery by NMR spectroscopy. This project aims to advance nuclear magnetic resonance (NMR) spectroscopy methods in the field of drug discovery. It addresses a long-standing bottleneck for medicinal chemists in drug development: the rapid determination of how ligand molecules bind to proteins, where they bind and their orientation in the binding site. The methods include techniques for the attachment of NMR tags to ligands and target proteins, installation of new unnatural amino acids in proteins, and software for automated assignment of NMR spectra and 3D structure modelling of proteins using sparse distance restraints measured by electron paramagnetic resonance (EPR) spectroscopy. The outcome is to benefit the early stages of drug discovery in the biotech industries.Read moreRead less
Learning Software Security Analysers with Imperfect Data. This project aims to systematically investigate next-generation learning-based software security analysis to detect vulnerabilities in real-world large-scale software. The expected learning-based foundation will support the handling of imperfect data in order to provide a precise, scalable and adaptive security analysis of the critical software components, thus capturing important security vulnerabilities missed by existing approaches. Th ....Learning Software Security Analysers with Imperfect Data. This project aims to systematically investigate next-generation learning-based software security analysis to detect vulnerabilities in real-world large-scale software. The expected learning-based foundation will support the handling of imperfect data in order to provide a precise, scalable and adaptive security analysis of the critical software components, thus capturing important security vulnerabilities missed by existing approaches. The success of this project will further enhance the international competitiveness of Australian research in this important field and will benefit any Australian industry and business where software systems are deeply-rooted, such as transportation, smart homes, medical devices, defence and finance.Read moreRead less
Secure and Resistant Blockchain for Financial and Business Applications. The aim of this project is to develop a practical secure blockchain technology for the booming applications in finance and business. This project expects to address the leading security threats to the current blockchain applications. The expected outcome is an executable secure and resistant blockchain prototype through the integration of the latest developed and customized techniques. The success of the project will dramat ....Secure and Resistant Blockchain for Financial and Business Applications. The aim of this project is to develop a practical secure blockchain technology for the booming applications in finance and business. This project expects to address the leading security threats to the current blockchain applications. The expected outcome is an executable secure and resistant blockchain prototype through the integration of the latest developed and customized techniques. The success of the project will dramatically benefit Australian people and government, especially for the Australian ICT industry for commercializing the research outputs. Read moreRead less
MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) ne ....MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) new methods for susceptibility diagnosis, 3) new defences that leverage privacy and utility. Data-oriented services are estimated to be valuable assets in the future. These techniques can help Australia gain cutting edge advantage in machine learning security and privacy and protect its intellectual property on these services.Read moreRead less