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FIT3185 · Privacy enhancing technologies

Official Handbook

2026 Handbook6 credit pointsLevel 3Faculty of Information Technology

Last checked: 23 Aug 2026 UTC

Overview

This unit encompasses a progression from foundational knowledge and comprehension to practical application, analysis, synthesis, and evaluation of advanced vulnerability analysis and mitigation techniques in the context of cloud security, operating systems security, reverse engineering, and side-channel analysis and mitigations.

Areas of study: Cybersecurity

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

prerequisite

  • FIT1093 — Cybersecurity tools and techniques
  • FIT2014 — Theory of computation

Joined by AND.

Learning outcomes

  1. Define the fundamental concepts of privacy-enhancing technologies, including differential privacy, post-quantum cryptography, zero-knowledge proofs, and secure multiparty computation
  2. Apply the principles of differential privacy to design privacy-preserving data collection and analysis techniques, ensuring that sensitive information remains confidential while maintaining data utility.
  3. Understand and evaluate post-quantum cryptographic algorithms in real-world scenarios to protect data and communications against potential quantum threats, showcasing proficiency in encryption and digital signatures.
  4. Be familiar with and assess zero-knowledge proofs to enhance privacy of applications such as secure authentication and verification processes.
  5. Create and analyse practical applications of secure multiparty computation, allowing multiple parties to jointly compute results while preserving the privacy of their inputs.
  6. Perform impact and risk assessment to provide responsible expert advice on (Australian) privacy policies, procedures and governance incorporating ethical considerations.
  7. Design and construct privacy-preserving algorithms and solutions by combining multiple privacy-enhancing technologies, demonstrating innovation in solving real-world privacy challenges.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

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