Lightweight security solutions for wearable healthcare sensor devices. The aim of this project is to develop new methods to secure the data and context associated with body-wearable health monitoring devices. The novelty of the scheme is in making the methods work on resource-poor devices, by combining new security capabilities derived from the operating environment with conventional cryptographic techniques. This project aims to increase the trust that medical practitioners and insurance provid ....Lightweight security solutions for wearable healthcare sensor devices. The aim of this project is to develop new methods to secure the data and context associated with body-wearable health monitoring devices. The novelty of the scheme is in making the methods work on resource-poor devices, by combining new security capabilities derived from the operating environment with conventional cryptographic techniques. This project aims to increase the trust that medical practitioners and insurance providers can place on health data from wearable devices, and showcase Australian innovation in developing world-class security solutions. The outcome of this project is expected to be the development and demonstration of ultra-lightweight algorithms and mechanisms that execute in wearable devices to safeguard the integrity of the data.Read moreRead less
Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving ....Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving this problem. It proposes to develop a set of effective methods for privacy-preserving data publication through combining randomisation with anonymisation, and for classifying the published data through uncertainty leveraging by probabilistic reasoning and accuracy lifting by inter-flow correlation analysis and active learning.Read moreRead less
Enhancing information credibility using mathematical prediction. The aim of this project is to develop theory, techniques, mathematical tools and practical algorithms for rumor detection and forecast in social media to enhance credibility of news, especially in time-sensitive situations and trending events. This project will significantly advance human knowledge of rumor formation, detection, and forecast, which will enable timely and efficient counter attacks. The outcomes from this project wil ....Enhancing information credibility using mathematical prediction. The aim of this project is to develop theory, techniques, mathematical tools and practical algorithms for rumor detection and forecast in social media to enhance credibility of news, especially in time-sensitive situations and trending events. This project will significantly advance human knowledge of rumor formation, detection, and forecast, which will enable timely and efficient counter attacks. The outcomes from this project will offer a reliable information environment for our society.Read moreRead less
Lightweight security framework for Low-Power Wide-Area Network (LPWAN). This project aims to design and implement a lightweight security framework for Low Power Wide Area Networks (LPWAN). Smart cities are estimated to have a worldwide market value of US$1.5 trillion by 2020, and LPWAN will provide connectivity to 90% of low bandwidth, smart city applications such as smart metres and smart buildings. Many of these applications are deployed in mission-critical infrastructure such as airport, trai ....Lightweight security framework for Low-Power Wide-Area Network (LPWAN). This project aims to design and implement a lightweight security framework for Low Power Wide Area Networks (LPWAN). Smart cities are estimated to have a worldwide market value of US$1.5 trillion by 2020, and LPWAN will provide connectivity to 90% of low bandwidth, smart city applications such as smart metres and smart buildings. Many of these applications are deployed in mission-critical infrastructure such as airport, train station, hospital and government campuses, which have strong security requirements. Before the ubiquitous deployment of such new technology, a strong security framework needs to be developed and implemented to minimise enormous economic and social consequences of future malicious attacks to LPWAN.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150101116
Funder
Australian Research Council
Funding Amount
$315,000.00
Summary
Leakage-Resilient and Quantum-Secure Authenticated Key Exchange Protocols. Authenticated key exchange protocols allow multiple parties to establish a common secret key over a public network, and are a central component of network security. Key-leakage and quantum attacks are two primary threats against the existing protocols. This project aims to fill the gap by developing new authenticated key exchange protocols which are secure against both attacks. The new models, theories, and techniques dev ....Leakage-Resilient and Quantum-Secure Authenticated Key Exchange Protocols. Authenticated key exchange protocols allow multiple parties to establish a common secret key over a public network, and are a central component of network security. Key-leakage and quantum attacks are two primary threats against the existing protocols. This project aims to fill the gap by developing new authenticated key exchange protocols which are secure against both attacks. The new models, theories, and techniques developed in this project will produce technologies essential for securing data communications in current and future computer networks, and hence directly contribute to improving cybersecurity for all Australians.Read moreRead less
Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?