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
Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and ....Searching Cohesive Subgraphs in Big Attributed Graph Data. The availability of big attributed graph data brings great opportunities for realizing big values of data. Making sense of such big attributed graph data finds many applications, including health, science, engineering, business, environment, etc. A cohesive subgraph, one of key components that captures the latent properties in a graph, is essential to graph analysis. This project aims to invent effective models of cohesive subgraphs and efficient algorithms for searching and monitoring cohesive subgraphs in big and dynamic attributed graphs from both structure and attribute perspectives. The methods, techniques, and prototype systems developed in this project can be deployed to facilitate the smart use of big graph data across the nation. Read moreRead less
Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for differen ....Modelling and Searching Cohesive Groups over Heterogeneous Graphs . Heterogeneous information networks (HINs) contain richer structural and semantic information represented as different types of objects and links. Searching cohesive groups from HINs finds many applications and also brings challenges at both conceptual and technical levels. This project aims to investigate the effective modelling of cohesive groups that take both homogeneous and heterogeneous information into account for different applications and devise efficient algorithms for searching and monitoring those cohesive groups based on different models. The methods, techniques, and evaluation systems developed in this project can be deployed to facilitate the smart use of heterogeneous information networks across the nation.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100768
Funder
Australian Research Council
Funding Amount
$380,446.00
Summary
Advanced coding techniques for fast failure recovery in storage systems. This project aims to improve the performance of distributed data storage systems during the recovery of node-failures using advanced repair techniques for the underlying erasure coding schemes. Reed-Solomon codes, employed in most of current storage systems, for example Google Colossus, Baidu's Atlas, Yahoo Object Store, and Facebook's f4, require extremely high network bandwidth and disk I/O for failure recovery. Expected ....Advanced coding techniques for fast failure recovery in storage systems. This project aims to improve the performance of distributed data storage systems during the recovery of node-failures using advanced repair techniques for the underlying erasure coding schemes. Reed-Solomon codes, employed in most of current storage systems, for example Google Colossus, Baidu's Atlas, Yahoo Object Store, and Facebook's f4, require extremely high network bandwidth and disk I/O for failure recovery. Expected outcomes of this project include significantly improved repair schemes for Reed-Solomon codes with respect to the network bandwidth and disk I/O. The project will benefit data storage service providers, governments, businesses, hospitals, and schools.Read moreRead less
Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobil ....Privacy-Preserving Location Based Queries. This project aims to develop efficient solutions for mobile users to consume location-based services (LBS) without revealing their locations. The project expects to demonstrate the effectiveness of the solutions using theoretic analysis and practical experiments. The expected outcomes are a multiparty trust model, techniques to distribute user location information among multiple location-based services, and a practical system to protect privacy in mobile environments. This should protect the privacy of individuals and increase users’ trust in location-based systems.Read moreRead less
Advanced error control coding techniques for scalable blockchains. The project aims to investigate the application of error-control coding theory in blockchains, focusing on reducing the storage, computation, and communication overheads, as well as increasing the throughput of blockchain networks. The ambition is to develop coding theory in a completely new territory: decentralised, untrusted, and peer-to-peer networks. The intended outcome is to greatly extend the current state of the art of th ....Advanced error control coding techniques for scalable blockchains. The project aims to investigate the application of error-control coding theory in blockchains, focusing on reducing the storage, computation, and communication overheads, as well as increasing the throughput of blockchain networks. The ambition is to develop coding theory in a completely new territory: decentralised, untrusted, and peer-to-peer networks. The intended outcome is to greatly extend the current state of the art of the theory of error-control codes, previously investigated only in the context of centralised architectures, where a server coordinates every task. Practically, the project should provide significant benefits in terms of cost-effectiveness of blockchains, increase in their processing speed, and security enhancement. Read moreRead less