Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop ....Personalised Privacy-Preserving Network Data Publishing System . Data sharing has become a driving force for many businesses in industrial sectors. This project aims to develop a privacy preserving network data publishing system that can preserve user privacy in a personalised way while maintaining maximal utility of the published data. To make accurate privacy preservation, this project will design novel learning models to derive accurate users’ correlation and their privacy intention, develop efficient privacy preserving algorithms to deal with static and dynamic network data sharing. The success of this project will benefit many industries and government agencies to reduce users’ privacy breaches, avoid illegal consequences of sharing data, and enhance these service providers’ service quality.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100165
Funder
Australian Research Council
Funding Amount
$443,847.00
Summary
Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, ....Evolving privacy and utility in data storage and publishing. This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, evolutionary algorithms, and knowledge-transfer strategies. Anticipated benefits include theoretical contributions to artificial intelligence, cyber security, distributed computation, and a service to eliminate data owners’ privacy concerns while guaranteeing the value of data in further utilization.Read moreRead less