Focusing on methods that protect user privacy by minimizing data exposure, controlling information flow, and enabling secure data use.
Privacy-Preserving and Enhancing Techniques encompass a range of cryptographic, system-level, and architectural approaches designed to ensure that sensitive information remains confidential throughout its lifecycle. Research in this area includes technologies such as differential privacy, secure multiparty computation, federated learning, homomorphic encryption, and zero-knowledge proofs. These techniques aim to allow meaningful data processing, analytics, and collaboration without revealing raw data or compromising individual privacy. The field also considers privacy-by-design principles, regulatory compliance, and emerging challenges in domains like AI, IoT, and cloud computing. As digital systems grow more complex and data-driven, these techniques are critical for balancing functionality with strong privacy guarantees.
Contact cyber@uq.edu.au for more information.
