Compression of distributed data: bridging the gap between theory and practice. In bushfire and tsunami early warning systems, environmental monitoring and healthcare applications, distributed sensors collect and transmit correlated data. This project will design novel data compression algorithms that exploit this correlation to dramatically increase the performance of existing networks and enable new applications.
Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes ....Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes are innovative technologies, guaranteeing accuracy and confidentiality of annotation results whilst protecting the privacy of data classification results. It enhances data-intensive outputs quality, which will benefit large data-intensive applications, such as cybersecurity protections via intrusion detection.Read moreRead less
Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the developmen ....Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the development of a cutting-edge technique to develop new intelligent applications in a decentralised and privacy-sensitive scenario. This game-changing research will advance current data mining and artificial intelligence research from centralised intelligence to decentralised intelligence with a collaboration network.Read moreRead less
Efficient Multi-key Homomorphic Encryption and Its Applications. Multi-key homomorphic encryption (MKHE) is a key technology that functions to allow multiple users to supply their private input for collaboration in the cloud while keeping the user data confidential. Unfortunately, it is very difficult to obtain efficient MKHEs. This project aims to overcome this challenge by enabling novel efficient MKHEs. The expected outcomes of this project are to develop innovative cryptographic technologies ....Efficient Multi-key Homomorphic Encryption and Its Applications. Multi-key homomorphic encryption (MKHE) is a key technology that functions to allow multiple users to supply their private input for collaboration in the cloud while keeping the user data confidential. Unfortunately, it is very difficult to obtain efficient MKHEs. This project aims to overcome this challenge by enabling novel efficient MKHEs. The expected outcomes of this project are to develop innovative cryptographic technologies which realise efficient MKHEs, together with their cryptographic libraries and practical applications in solving industry problems. This will provide direct economic benefits to Australian industry through the enablement of advanced technologies and low-cost business solutions which are developed in Australia.Read moreRead less
Enabling Anonymity and Privacy for Blockchain Technology in a Quantum World. Blockchain is a promising technology in the digital world today. However, existing approaches for enabling blockchain applications, particularly with privacy protection and anonymity, are vulnerable to quantum computer attacks. This project aims to enable novel cryptographic mechanisms together with their cryptographic libraries for protecting blockchain in the quantum world, hence, post-quantum secure blockchain. The e ....Enabling Anonymity and Privacy for Blockchain Technology in a Quantum World. Blockchain is a promising technology in the digital world today. However, existing approaches for enabling blockchain applications, particularly with privacy protection and anonymity, are vulnerable to quantum computer attacks. This project aims to enable novel cryptographic mechanisms together with their cryptographic libraries for protecting blockchain in the quantum world, hence, post-quantum secure blockchain. The expected outcomes of this project include innovative technologies, as well as secure and practical post-quantum protocols for protecting future blockchain applications. This will provide economic and social benefits to Australian industry through the enablement of advanced technologies which are developed in Australia.Read moreRead less
New high-performance iterative error correction codes. This project develops new error correction codes to underpin the success of next-generation communications technologies. The nature of the project presents significant potential for project outcomes to be beneficial to the Australian telecommunications industry in a wide range of application areas from optical communication to digital broadcasting.
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Radio resources and inter-cell interference management in smart grid wireless access networks. Wireless communications is the key enabler of smart grids. The project will deliver novel radio resource allocation protocols with low latency, high radio spectrum efficiency and reliability for radio access networks in smart grids. The project will develop new technologies with a potential to be implemented in future Long Term Evolution (LTE) machine-to-machine (M2M) standards.
Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project ....Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project expects to provide practical data analysis approaches and establish the theoretical foundations for data mining with multiple sources of brain data.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101808
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims ....Genetic Programming for Big Data Analytics. The project aims to extend a powerful machine learning method, called genetic programming and also developing a new concept called Alpha program, for big data analytics. This project expects to generate a new approach by finding a systematic approach to develop gene structures using information theory. By borrowing the best genes from the population of programs, the Alpha program concept will be developed for the first time. The proposed approach aims to enhance genetic programming for many practical problems. I contend that not only finding better tools for big data analytics is in the best interest of machine learning and big data communities, it also provides significant benefits for other communities and industries in Australia.Read moreRead less