Dr. Paul Tyler
Principle Research Engineer & Data Privacy Team Leader
Information Security and Privacy Group
Software and Computational Systems Program
phone: +61 2 9490 5908
Paul is a Principle Research Engineer with Data61’s Information Security and Privacy Group, leading the Data Privacy team. Paul has been with Data61 (and formally NICTA) since 2004 and currently has a focus on bringing privacy and security research to the broader data ecosystem. The Data Privacy Team and Information Security and Privacy Group in general have a focus on understanding privacy risk in data, and researching and developing methods for controlling risk, particularly with proven level of privacy. The team is also developing tools to help in this task. Paul has experience in helping Australian government agencies and companies in understanding privacy risk in their data and helping them take action to control that risk. This is particularly true of re-identification risk and understanding how that risk is reduced through the applications of common and not so common methods of data treatment.
Prior to his work in privacy, Paul has also worked in bringing research, project management and technical knowledge to Transport for NSW to deploy the Cooperative Intelligent Transport Initiative (CITI), a connected vehicles trial in the Illawarra region. Previous he has also worked on projects such as traffic state estimations from loop detectors at signalised roundabouts, investigations into adaptive traffic control systems and video tracking of vehicles. Previous to Data61, Paul worked at the Australian Nuclear Science and Technology Organisation (ANSTO) fulfilling a scientific computing and research engineering role with a particle accelerator.
Paul has considerable experience in systems engineering, computer systems administration and software development as well as performing project management roles. Paul has a PhD in Computer Science and a Bachelor of Science both from the University of Sydney.
Paul is currently involved in a number of project around privacy and re-identification risk in data and applying transformations to data that result in data with proven privacy properties.