[Cite as http://purl.org/au-research/grants/arc/DP150104871]
Researchers Prof Hong Shen
Brief description Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving this problem. It develops a set of effective methods for privacy-preserving data publication through combining randomization with anonymization, and for classifying the published data through uncertainty leveraging by probabilistic reasoning and accuracy lifting by inter-flow correlation analysis and active learning.
Funding Amount 340300
Funding Scheme Discovery Projects