Understanding that a dataset contains sensitive information is only the first step—knowing how much risk it carries is what allows you to act. In this topic, you’ll learn how to define, measure, and evaluate privacy risks in a structured, data-driven way. You’ll explore key attributes of data—such as uniqueness, uniformity, and correlation—that increase the risk of identifying individuals, even in seemingly anonymised datasets. From personally identifiable information (PII) to linkability between data sources, this topic introduces you to the critical variables that shape privacy exposure. You’ll also be introduced to standard metrics and evaluation techniques that help quantify privacy risk—giving you the tools to assess the vulnerability of information systems and datasets, and make informed decisions about how to mitigate those risks.

Releasing or sharing data can unlock immense value—but without careful de-identification, it can also expose individuals to serious privacy risks. In this topic, you’ll explore the core challenge of privacy-preserving data release and examine the models that aim to balance data utility with confidentiality. You’ll be introduced to foundational privacy models like k-anonymity, l-diversity, and t-closeness, each designed to limit re-identification risk while preserving analytical usefulness. You’ll also discover how attackers can exploit weaknesses in these models—through linkage, homogeneity, and background knowledge attacks—reinforcing the importance of choosing the right model for each context. By engaging with real-world examples and structured methods, you’ll gain the practical skills to anonymise datasets responsibly and defend against privacy breaches.

As data breaches and re-identification attacks become more sophisticated, traditional privacy techniques—like k-anonymity—are increasingly being challenged. In this topic, you'll explore why the world is shifting from probable privacy (where protections are assumed) to provable privacy (where protections are mathematically guaranteed). You’ll be introduced to the concept of differential privacy—a rigorous and provable framework that ensures individual data remains protected even when aggregated insights are published. You'll examine how different models and mechanisms of differential privacy work, such as adding noise to query results or applying privacy budgets, and understand how these techniques are shaping the future of secure data analysis.

In an increasingly data-driven world, linking information across datasets can unlock powerful insights—whether it's matching patient records across hospitals or tracking customer behaviour across services. However, combining data from different sources often risks exposing sensitive personal information. This topic introduces the concept of privacy-preserving record linkage (PPRL)—a set of techniques designed to match and merge records across datasets without compromising individual privacy. You'll explore why traditional record linkage methods are vulnerable, and how privacy-preserving approaches use cryptographic and statistical techniques to ensure that sensitive information remains protected during the linkage process.

In today’s complex data environment, theory alone is not enough—practical tools and internationally recognised frameworks are essential for implementing strong privacy and security measures across any organisation. This topic equips you with the hands-on skills to use industry-standard tools and understand the global standards shaping data protection practices. You’ll explore and apply real-world tools for de-identifying datasets, protecting sensitive information through encryption, and securing data both at rest and in motion. From open-source privacy tools to enterprise-grade security applications, this topic demonstrates how technology supports compliance and reduces risk in practice. In addition, you’ll gain critical awareness of global privacy frameworks, including the Australian Bureau of Statistics (ABS) Five Safes Framework and the NIST Privacy Framework, learning how these standards guide decision-making and accountability in data handling.