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
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
Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures fo ....Responsible modelling respecting privacy, data quality, and green computing. With the unprecedented growing impact of data on science, the economy and society, there comes the need for responsible data science practices which are accountable for the social good. This project aims to investigate the challenging problem of how to provide responsible data management, spanning across privacy-aware data exploration, resilient modelling to cope with imperfect data, and efficient model architectures for resource-constrained environments. This will be achieved by developing theories and techniques for complex real-world multi-modal data retrieval throughout the data life-cycle. The expected outcomes will significantly contribute to building capability in emerging technologies in the context of responsible data science. Read moreRead less
Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and wo ....Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and worldwide to reliably identify potential privacy risks and issues on their applications. The intended outcomes should endow data controllers with the capability of evidencing their compliance of data protection legislations such as Australia Privacy Act 1988 and EU General Data Protection Regulation (GDPR).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
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
Provably Secure Cryptography Techniques: Effective, Elegant, and Economic. This project aims to contribute to advanced knowledge and techniques to remove relaxed proof factors from provable security. Cryptography nowadays can be proven secure and must be provably secure before being adopted for data protection. Until today, most cryptography schemes are still using some relaxed proof factors to prove security, but using these relaxed factors was risky. The expected outcomes are proof methodolog ....Provably Secure Cryptography Techniques: Effective, Elegant, and Economic. This project aims to contribute to advanced knowledge and techniques to remove relaxed proof factors from provable security. Cryptography nowadays can be proven secure and must be provably secure before being adopted for data protection. Until today, most cryptography schemes are still using some relaxed proof factors to prove security, but using these relaxed factors was risky. The expected outcomes are proof methodologies for researchers to prove security in an easy way (effective), cryptography techniques for proving security without any relaxed proof factors for cryptography schemes (elegant), and more practical cryptography schemes with elegant proofs to enable Australians to receive benefit from secure data protection (economic).
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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
Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares. Data exfiltration is a serious threat as highlighted in recent leakage of sensitive data that resulted in huge economic losses as well as unprecedented breaches of national security. The aim of this project is to develop a comprehensive and robust solution for detection and prevention of sensitive data exfiltration attempts by malware and unauthorised human users. Expected outcomes include scalable monitoring methods an ....Preventing Exfiltration of Sensitive Data by Malicious Insiders or Malwares. Data exfiltration is a serious threat as highlighted in recent leakage of sensitive data that resulted in huge economic losses as well as unprecedented breaches of national security. The aim of this project is to develop a comprehensive and robust solution for detection and prevention of sensitive data exfiltration attempts by malware and unauthorised human users. Expected outcomes include scalable monitoring methods and efficient algorithms that will be able to prevent real-time exfiltration and identify previously undetected exfiltration of sensitive data. This should provide significant benefits to governments, defence networks as well as businesses and health sectors, as it will protect them from sophisticated cyber attacks.
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Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less