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
Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while pres ....Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while preserving the data privacy. These tools should provide significant benefits to the privacy of cloud users, as well as financial and reputation benefits to the IT industry, by significantly reducing the likelihood of massive user data privacy breaches in the event of a cyber-hacking attack on the cloud server.Read moreRead less
X-ray Ghost Imaging and Tomography. This project aims to achieve safer, faster, and cheaper 3D X-ray imaging through a technique known as ghost imaging. X-ray imaging provides valuable information about internal structures, however, X-rays are carcinogenic and exposure (or dose) should be limited. Ghost imaging is an unconventional technique developed with visible light that has many potential benefits over conventional imaging. This research group are world leaders in ghost imaging and expect t ....X-ray Ghost Imaging and Tomography. This project aims to achieve safer, faster, and cheaper 3D X-ray imaging through a technique known as ghost imaging. X-ray imaging provides valuable information about internal structures, however, X-rays are carcinogenic and exposure (or dose) should be limited. Ghost imaging is an unconventional technique developed with visible light that has many potential benefits over conventional imaging. This research group are world leaders in ghost imaging and expect to develop software and hardware techniques to realise its potential and extend it to ghost tomography. The focus of this project is on reducing cancer risk in medical imaging, and allowing real-time quality control for 3D printing in safety-critical industries such as aerospace.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE240100063
Funder
Australian Research Council
Funding Amount
$1,900,000.00
Summary
Scanning Transmission Electron Microscope for Beam-Sensitive Materials. This project aims to establish a transmission electron microscopy facility for the high-throughput characterisation of delicate materials, at the atomic scale and a broad range of temperatures. Unique in Australia, this capability will enable the location and type of atoms critical to materials properties to be determined for materials as diverse as lithium-bearing minerals, next-generation solar cells and drug-delivery agen ....Scanning Transmission Electron Microscope for Beam-Sensitive Materials. This project aims to establish a transmission electron microscopy facility for the high-throughput characterisation of delicate materials, at the atomic scale and a broad range of temperatures. Unique in Australia, this capability will enable the location and type of atoms critical to materials properties to be determined for materials as diverse as lithium-bearing minerals, next-generation solar cells and drug-delivery agents. In this way it will foster the engineering of new materials for addressing current challenges in energy, environment, transport, health and manufacturing. This will be a national, open access facility for use by research institutions and industry, and for training the next generation of postgraduate students.Read moreRead less
DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting th ....DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system. The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems. Read moreRead less
Design and verification of correct, efficient and secure concurrent systems. This project aims to provide methods for the design and verification of correct, secure and efficient concurrent software that are scalable and mechanised. Computers with multiple processors are now the norm and are used in a wide range of safety, security and mission critical software applications such as transport, health and infrastructure. These multi-core architectures have the potential to lead to important effici ....Design and verification of correct, efficient and secure concurrent systems. This project aims to provide methods for the design and verification of correct, secure and efficient concurrent software that are scalable and mechanised. Computers with multiple processors are now the norm and are used in a wide range of safety, security and mission critical software applications such as transport, health and infrastructure. These multi-core architectures have the potential to lead to important efficiency gains, but can introduce complex and error-prone behaviours that cannot be managed using traditional software development approaches. This project will produce better, scalable and mechanised methods for the design and verification of such software which is expected to reduce the prevalence of failures in efficient, modern software.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101100
Funder
Australian Research Council
Funding Amount
$425,613.00
Summary
Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health c ....Towards Robotic Empathy: A human centred approach to future AI machines. The project aims to equip future robots with empathy by developing computational models which can leverage from verbal and non-verbal cues. With recent advances in artificial intelligence research, robots now have better cognitive and function skills, but they lack socio-emotional skills. Since these robots are expected to provide assistance to humans across different domains including rehabilitation, education and health care, empowering them with empathetic abilities is important for their success. The project will advance fundamental research in machine learning, affective computing and artificial intelligence to model human behavior, personality traits and emotions for an empathetic human-robot interaction.Read moreRead less
Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili ....Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.Read moreRead less
Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this i ....Improving the specificity of affective computing via multimodal analysis. This project aims to develop multimodal affective sensing techniques that can sense very subtle expressions in human moods and emotions. Much research in affective computing has investigated ways to improve the sensitivity of affect sensing approaches, resulting in more accurate estimates of affective states such as emotions or mood. What remains unsolved so far is the issue of specificity. This project will address this issue through novel analyses of very subtle cues in facial and vocal expressions of affect embedded in a multimodal deep learning framework. Current approaches can successfully assist in binary classification tasks. This project will tackle the much more difficult problem of developing advanced affective sensing technology to simultaneously handle homogeneous and heterogeneous affect classes as well as continuous range estimates of affect intensity.Read moreRead less