Data Security & Information Privacy

Macquarie University via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

14 hours 53 minutes
Sessions

On-Demand
Subtitles

English

Found in

Overview

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Data Security and Information Privacy | Protect Identities. Preserve Trust. Power Compliance. In a digital-first world, data is more than an asset—it’s a liability if not protected. With rising breaches, stricter global regulations, and growing public scrutiny, organisations must do more than lock down their systems—they must actively secure their data and ensure privacy by design. From Risk to Resilience Developed by the Cyber Skills Academy at Macquarie University—ranked in the top 1% of universities globally and recognised as Australia’s leading cyber security school—this course offers a powerful, hands-on exploration of the principles, methods, and technologies used to manage data security and privacy in complex, real-world environments. You’ll gain critical skills in: • Identifying threats, risks, and vulnerabilities in modern information systems and networks. • Evaluating privacy risk using quantifiable metrics, and applying de-identification models like k-anonymity, l-diversity, and t-closeness. • Navigating the shift from probabilistic to provable privacy with differential privacy techniques. • Linking datasets securely with privacy-preserving record linkage methods. • Using cutting-edge tools for data encryption, anonymisation, and secure sharing. • Applying international standards and frameworks like the ABS Five Safes and the NIST Privacy Framework to ensure compliance and global alignment. Where Ethics Meets Engineering Whether you're a compliance leader, analyst, data scientist, or privacy-conscious tech professional, this course empowers you to embed privacy as a core part of your data strategy—not just a legal requirement. You’ll develop the confidence to protect identities, reduce organisational risk, and maintain the trust of your customers and communities. Learn from global leaders. Protect what matters. Shape a privacy-first future.

Syllabus

Threats, Risks and Vulnerabilities

Before organisations can defend the integrity and privacy of their information, they must first understand what they're up against. This foundational topic introduces learners to the evolving landscape of digital risk—from technical vulnerabilities in information systems to the broader implications of data privacy breaches. You'll explore the weak points that make networks and systems attractive to attackers, from exposed endpoints and unencrypted data flows to poor access controls and policy gaps. More importantly, you'll develop a working definition of "security" and "privacy" within different organisational and regulatory contexts—recognising how these concepts shift across industries and geographies. With a spotlight on real-world data breaches and systemic failures, this topic lays the groundwork for identifying, classifying, and contextualising common risks in modern digital ecosystems. You'll also unpack the unique challenges of protecting sensitive information in a world where data flows freely and threats are constantly evolving.

Privacy Risk Quantification and Evaluation

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.

De-identification Methods for Data Release: From k-anonymity to t-closeness

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.

Probable Privacy to Provable Privacy

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.

Privacy-Preserving Record Linkage

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.

Privacy and Security Tools, Use Cases, and Frameworks

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.