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
Scalable & Accountable Privacy-Preserving Blockchain with Enhanced Security. This project aims to address the scalability and accountability of privacy-preserving blockchain by advancing cryptographic techniques. This project expects to develop scalable protocols for privacy-preserving blockchain while also adding accountability for authority to trace cyber crime activities, which is a missing piece in any state-of-the-art public blockchain system. Expected outcomes of this project include not o ....Scalable & Accountable Privacy-Preserving Blockchain with Enhanced Security. This project aims to address the scalability and accountability of privacy-preserving blockchain by advancing cryptographic techniques. This project expects to develop scalable protocols for privacy-preserving blockchain while also adding accountability for authority to trace cyber crime activities, which is a missing piece in any state-of-the-art public blockchain system. Expected outcomes of this project include not only practical solutions for protecting sensitive data recorded in blockchain but also crucial techniques to make the blockchain accountable for practical applications with enhanced security. This project provides significant benefits, such as building a trusted environment for sensitive transactions in the digital economy.Read moreRead less
Preventing sensitive data exfiltration from insiders . Confidential data such as military secrets or intellectual property must never be disclosed outside the organisation; formally protecting data exfiltration from insider attacks is a major challenge. This project aims to develop a pattern matching based systematic methodology for data exfiltration in database systems. We will devise highly accurate detection tools and secure provenance techniques that can effectively protect against insider a ....Preventing sensitive data exfiltration from insiders . Confidential data such as military secrets or intellectual property must never be disclosed outside the organisation; formally protecting data exfiltration from insider attacks is a major challenge. This project aims to develop a pattern matching based systematic methodology for data exfiltration in database systems. We will devise highly accurate detection tools and secure provenance techniques that can effectively protect against insider attacks. The outcomes of the project will incorporate new security constraints and policies raised by emerging technologies to enable better protection of sensitive information. Read moreRead less
Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and ....Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and inventing non-removable watermarks on AI models. The outcomes are new tools for securing AI-based FinTech systems before deployment and tools for IP violation forensics post-deployment. Such capabilities are beneficial by improving the security and safety of FinTech systems and other nationally critical AI systems.Read moreRead less
Development of Cryptographic Library and Support System. The protection of the whole cyber space relies on a foundation of cryptography. Cryptographic components of apps authenticate remote parties and secure the communications. However, cryptographic misuse has become a most common issue in development of security component, affecting up to 90% of apps!
This project aims to research, design and develop a crypto library. The innovation of this project lays in three aspects: (1) we will develop ....Development of Cryptographic Library and Support System. The protection of the whole cyber space relies on a foundation of cryptography. Cryptographic components of apps authenticate remote parties and secure the communications. However, cryptographic misuse has become a most common issue in development of security component, affecting up to 90% of apps!
This project aims to research, design and develop a crypto library. The innovation of this project lays in three aspects: (1) we will develop a self-contained, reliable, compatible and verifiable crypto library; (2) we will develop security test software automatically to test and verify security of codes; and (3) we will provide intelligent decision support through argumentation to help developers to apply the library efficiently and correctly.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100166
Funder
Australian Research Council
Funding Amount
$424,709.00
Summary
Enabling Energy Self-Sufficient and Secure Internet of Things. This project aims to develop novel resource management and transmission techniques to enable an energy self-sufficient and secure Internet of Things by utilising energy harvesting technology and robust physical-layer security approach. This project expects to generate new knowledge to address current challenges around energy self-sufficiency and data confidentiality protection capabilities. Expected outcomes include efficient algorit ....Enabling Energy Self-Sufficient and Secure Internet of Things. This project aims to develop novel resource management and transmission techniques to enable an energy self-sufficient and secure Internet of Things by utilising energy harvesting technology and robust physical-layer security approach. This project expects to generate new knowledge to address current challenges around energy self-sufficiency and data confidentiality protection capabilities. Expected outcomes include efficient algorithms and prototypes for long-lasting Internet of Things systems. This should provide significant benefits, including the improved self-sustainability and security critical to realising the Internet of Things’ potential to contribute to enhanced health service delivery and factory automation for Industry 4.0.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
Encrypted, Distributed, and Queryable Data Store: Framework and Realisation. This project aims to design an encrypted, distributed, and queryable data store. Distributed data stores are used for a broad spectrum of applications. While creating unprecedented opportunities, long-standing data security and privacy concerns are yet to be tackled. This project expects to propose a new architecture for encrypted data stores, and devise practical query processing functions over encrypted and distribute ....Encrypted, Distributed, and Queryable Data Store: Framework and Realisation. This project aims to design an encrypted, distributed, and queryable data store. Distributed data stores are used for a broad spectrum of applications. While creating unprecedented opportunities, long-standing data security and privacy concerns are yet to be tackled. This project expects to propose a new architecture for encrypted data stores, and devise practical query processing functions over encrypted and distributed data records. The intended outcome should bring users confidence for the secure adoption of cloud data storage services and significantly benefit enterprises that demand guaranteed protection on their proprietary data.Read moreRead less
Decentralised Data Management for Edge Caching Systems in 5G. This project aims to deliver a suite of decentralised data management approaches to facilitate practical edge caching systems in the 5G mobile edge computing (MEC) environment. Edge caching offers great promises for Australia's post-COVID economic recovery and resilience with the ability to enable real-time mobile and IoT software applications in various domains, e.g., telehealth, online learning/working, advanced manufacturing, etc. ....Decentralised Data Management for Edge Caching Systems in 5G. This project aims to deliver a suite of decentralised data management approaches to facilitate practical edge caching systems in the 5G mobile edge computing (MEC) environment. Edge caching offers great promises for Australia's post-COVID economic recovery and resilience with the ability to enable real-time mobile and IoT software applications in various domains, e.g., telehealth, online learning/working, advanced manufacturing, etc. This project tackles new and urgent challenges in edge data storage, manipulation, maintenance, and protection with optimisation, distributed consensus, graph analytics, and cryptography techniques. The outcomes should build the pillars of edge caching systems and promote Australia's 5G software innovations.Read moreRead less
Privacy preserving and data utility in outsourced systems. Making the best tradeoff between data privacy and utility is a vital challenge in privacy-preserving outsourcing environments. This project aims to develop a balanced distributed framework to achieve the best utility of outsourced data while protecting private information. The framework consists of general structure of distributed evolutionary algorithms and a predefined topology for high optimization efficiency and a dynamic groupin ....Privacy preserving and data utility in outsourced systems. Making the best tradeoff between data privacy and utility is a vital challenge in privacy-preserving outsourcing environments. This project aims to develop a balanced distributed framework to achieve the best utility of outsourced data while protecting private information. The framework consists of general structure of distributed evolutionary algorithms and a predefined topology for high optimization efficiency and a dynamic grouping recombination model. The project outcomes will be beneficial to applications in the nation as it incorporates new privacy constraints and utility requirements raised by emerging technologies to enable better protection of sensitive information and maximal data utility in outsourced systems. Read moreRead less