The candidates will work effectively as part of a multi-disciplinary collaborative research team, to undertake independent scientific investigations and carry out associated tasks under the guidance of senior industry researchers and academics. Quality research outcomes and publications in high impact journals and conferences will be expected.
With the rapid adoption of the Internet of Things (IoT) across a range of critical industries (e.g. agriculture), cybersecurity and sensor/device tampering incidences are on the rise and present a significant challenge. The PhD candidate will undertake research into security and integrity of data at the point of inception (edge) in IoT environments where data from devices, sensors or users is collected, processed, and analyzed to support data-driven business objectives. Sequel to this is the prevention against leakage and unauthorized sharing of Personal Identifiable Information (PII) and compliance with privacy regulations. This project aims to develop an integrity and privacy-preserving data storage and analytics framework. The main skills required are a good understanding of embedded systems, IoT security and privacy, and machine learning with solid programming skills in C/Python.
Federated Learning (FL) is a modern approach for training machine learning models across multiple, decentralised systems, each holding local, potentially sensitive data. The PhD candidate will work on Computing on Encrypted Data technologies, in particular Secure Multiparty Computation (MPC), to realize secure aggregation for FL. This work will span cryptographic, algorithmic and hardware acceleration aspects. The main skills required are a good understanding of cryptographic algorithms, hardware accelerated computing mechanisms (e.g., FPGAs) for modern cryptography, and solid programming skills in C/Python. Familiarity with contemporary machine learning models is also highly desirable.
Failure to develop trust relationships between primary service providers, end-users and 3rd-party service providers is among the most important reasons for failures of innovation and transaction platforms. The PhD candidate will work on the design and development of methods and infrastructure for trusted digital cleanroom/marketplaces. Privacy-preserving computing technologies (PPCTs) will be employed to preserve ownership, guarantee sovereignty, and create confidence that an asset will deliver value once acquired. Skills required are a good understanding of PPCTs such as Secure Multiparty Computation (MPC) and Trusted Execution Environments (TEEs) as well as solid programming skills in C/Rust and Python. Familiarity with anonymization techniques, e.g. for differential privacy, as well as with contemporary machine learning models is also highly desirable.
$28,854 per annum (2022 rate, indexed annually)
University of Technology Sydney
The scholarships are available to eligible domestic/international candidates to undertake 3-year full-time PhD program. The scholarships are comprised of a Tuition Fee Offset and a Living Allowance Stipend. The value and tenure of the scholarships is a full-time stipend rate of $28,854 per annum (2022 rate, indexed annually).
UTS is seeking applicants with outstanding academic merit with prior research experience in modern cryptography and hardware acceleration, machine learning, trusted digital cleanrooms/marketplaces and IoT security and privacy. To be competitive in winning a scholarship, you will need the following: