Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expecte ....Data sharing with strong privacy against inference attacks. This project aims to develop theories and techniques for strong protection of personal information in sharing large datasets such as national health data or census records. It intends to achieve this through developing new information theoretic methods for synthesising datasets with proven high fidelity and protection against re-identification and inference attacks, where attackers try to learn probability of sensitive data. The expected outcomes are algorithms for public and private sector data curators to dial up or down their data access arrangements based on privacy risks and fidelity demands linked with different data types and uses. This project intends to enable Australians to securely benefit from valuable data in decision making.Read moreRead less
Taming Large-Volume Dynamic Graphs in the Cloud. This project aims to develop efficient and scalable algorithms to process large-volume dynamic graphs in the cloud. The project expects to address key challenges and lay theoretical foundations in large-volume dynamic graph processing, which plays an important role in developing general-purpose, real-time structural search engines. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big graphs that ....Taming Large-Volume Dynamic Graphs in the Cloud. This project aims to develop efficient and scalable algorithms to process large-volume dynamic graphs in the cloud. The project expects to address key challenges and lay theoretical foundations in large-volume dynamic graph processing, which plays an important role in developing general-purpose, real-time structural search engines. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big graphs that evolve rapidly over time. These enable users to monitor and analyse structural information in large dynamic networks in real time. The project expects to open up a new research direction for graph processing to enrich frontier technologies and benefit many key applications in Australia.Read moreRead less
Efficient and Scalable Processing of Dynamic Heterogeneous Graphs . This project aims to develop efficient and scalable algorithms to process large-scale dynamic heterogeneous graphs where graph nodes and edges are of multiple types and the graph structure updates dynamically. Key challenges are expected to be addressed including complex structure, high speed, and large volume of dynamic heterogeneous graphs. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incre ....Efficient and Scalable Processing of Dynamic Heterogeneous Graphs . This project aims to develop efficient and scalable algorithms to process large-scale dynamic heterogeneous graphs where graph nodes and edges are of multiple types and the graph structure updates dynamically. Key challenges are expected to be addressed including complex structure, high speed, and large volume of dynamic heterogeneous graphs. The anticipated outcomes include novel computing paradigms, algorithms, indexing, incremental computation, distributed algorithms as well as a system prototype to demonstrate the practical value. Success of this project will open up a new research direction to enrich frontier technologies and benefit many key applications in Australia including cybersecurity, e-commerce, health and social networks.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Effective, efficient and scalable processing of big geo-textual streams. This project aims to develop novel approaches to realise the value of geo-textual data, which carries both location and textual information. The project expects to address three key challenges brought by massive volumes and high speeds of big geo-textual streams: better user experiences; increased efficiency; and greater scalability in query processing. The project should provide individuals, business and government agencie ....Effective, efficient and scalable processing of big geo-textual streams. This project aims to develop novel approaches to realise the value of geo-textual data, which carries both location and textual information. The project expects to address three key challenges brought by massive volumes and high speeds of big geo-textual streams: better user experiences; increased efficiency; and greater scalability in query processing. The project should provide individuals, business and government agencies with the ability to unlock key values in the overwhelming volume of high-speed, big geo-textual streams for important usage in many emerging key applications, such as social media analytics, location-based services, social networks, e-marketing and cybersecurity.Read moreRead less
Advanced search of cohesive subgraphs in big graphs. This project aims to study advanced cohesive subgraph searches, as well as design efficient and scalable techniques to conduct such searches. Cohesive subgraph search over big graphs is demanded by many applications, such as risk management, analysis of users’ behaviours, cybersecurity, crime detection, social marketing and community search. This project will develop, analyse, implement, and evaluate novel indexing and data processing techniqu ....Advanced search of cohesive subgraphs in big graphs. This project aims to study advanced cohesive subgraph searches, as well as design efficient and scalable techniques to conduct such searches. Cohesive subgraph search over big graphs is demanded by many applications, such as risk management, analysis of users’ behaviours, cybersecurity, crime detection, social marketing and community search. This project will develop, analyse, implement, and evaluate novel indexing and data processing techniques to support a set of advanced cohesive subgraph searches. This will provide significant benefits to many applications such as the next generation of fintech, cybersecurity, e-commerce, crime detection and social network analysis.Read moreRead less
Big Data Machines: Internet-Scale Machine Learning Techniques to Combat the Curse of Big Data. The advent of “big data” in business, government, science, social networks, the internet, etc. creates opportunity in business and commercial domains. Big data also raises issues of increasing volume, variety, dimensionality and categories in open domain big data applications, which this project will solve by developing novel machine learning techniques, including theoretical foundations, a big data ma ....Big Data Machines: Internet-Scale Machine Learning Techniques to Combat the Curse of Big Data. The advent of “big data” in business, government, science, social networks, the internet, etc. creates opportunity in business and commercial domains. Big data also raises issues of increasing volume, variety, dimensionality and categories in open domain big data applications, which this project will solve by developing novel machine learning techniques, including theoretical foundations, a big data machine learning framework and open source website. The outcomes of this project will provide frontier technologies for big data analysis that will have social and economic impact in such areas as social media computing, bioinformatics and business intelligence, and enhance Australia’s global position in the pattern recognition and data mining communities.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less
Memetic algorithms for multiobjective optimisation problems in bioinformatics. Many questions of paramount importance in life sciences can be formulated as optimisation problems but using just a single criterion can be misleading. This project will address this problem using multiobjective optimisation and leveraging Australia's investment in supercomputing with algorithms that mimic evolutionary processes in silico.
Homomorphic cryptography: computing on encrypted data. This project is driven by the groundbreaking applications of a new cryptographic technology that allows analysis of encrypted (scrambled) data without needing to decrypt (unscramble) it first. The results of this project can be used to enable secure remote data storage, electronic auctions and voting, and protecting medical records.