BigPrivacy: Scaling privacy preservation for big data applications on cloud. This project aims to research scalable privacy preservation for big data applications on cloud. Privacy preservation is a major concern for big data applications on cloud, such as health data analysis where user privacy must be preserved. Scalable solutions can preserve privacy so that data analysis such as health diagnosis can be performed quickly. The expected deliverable is a unified scalable privacy preservation fra ....BigPrivacy: Scaling privacy preservation for big data applications on cloud. This project aims to research scalable privacy preservation for big data applications on cloud. Privacy preservation is a major concern for big data applications on cloud, such as health data analysis where user privacy must be preserved. Scalable solutions can preserve privacy so that data analysis such as health diagnosis can be performed quickly. The expected deliverable is a unified scalable privacy preservation framework with associated algorithms and its prototype, which cloud systems can deploy for big data applications.Read moreRead less
Detecting Firmware Vulnerabilities in Smart Home Devices. 83% of Australians have smart home devices. 47% claim they have three or more. These devices are easily targeted by cyber-attacks, and searching for their vulnerabilities has become more crucial than ever. Our industry partner GPG is actively looking for ways to detect vulnerabilities in their smart home products, but have not found any existing methods that satisfy three critical requirements: 1) massive search, 2) cross platform detecti ....Detecting Firmware Vulnerabilities in Smart Home Devices. 83% of Australians have smart home devices. 47% claim they have three or more. These devices are easily targeted by cyber-attacks, and searching for their vulnerabilities has become more crucial than ever. Our industry partner GPG is actively looking for ways to detect vulnerabilities in their smart home products, but have not found any existing methods that satisfy three critical requirements: 1) massive search, 2) cross platform detection, and 3) finding unseen vulnerabilities. We therefore propose to use a series of new techniques such as efficient in-memory fuzzing, conditional formulas, and transfer learning to solve the above challenges. The project outcomes will help Australia gain cutting edge techniques in vulnerability detection. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100950
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Building intelligence into online video services by learning user interests. This project aims to build an intelligent video streaming service by characterising users’ view interest patterns and predict user interest changes through learning data from Internet to address the challenge caused by astronomic video population. The outcomes of the project will be of great values for users and our society by intelligently filtering out valueless, harmful, illegal and unwanted videos in advance.