Knowing a dataset holds sensitive information is one step; quantifying its risk enables action. This topic teaches you to define, measure, and evaluate privacy risks with data-driven methods. You'll examine data attributes like uniqueness and correlation that increase identification risk, even in anonymized datasets. From PII to data source linkability, this module covers variables shaping privacy exposure. You'll gain standard metrics and evaluation techniques to quantify privacy risk, assessing information system and dataset vulnerability to mitigate risks. Focus on applying these metrics to real-world data.

Sharing data offers value, but without de-identification, it risks privacy. This topic addresses privacy-preserving data release, balancing utility with confidentiality. You'll learn foundational privacy models: k-anonymity, l-diversity, and t-closeness, designed to limit re-identification risk. Discover how attackers exploit model weaknesses through linkage and background knowledge attacks. Gain practical skills to anonymize datasets responsibly and defend against privacy breaches. Practice applying these models to various data scenarios.

With sophisticated data breaches, traditional privacy methods face challenges. This topic explains the shift from probable to provable privacy, where protections are mathematically guaranteed. You'll learn about differential privacy, a rigorous framework protecting individual data even in aggregated insights. Examine how differential privacy models work, including adding noise to query results and applying privacy budgets. Understand how these techniques shape secure data analysis. Focus on the mathematical foundations of differential privacy.

Linking data across datasets offers insights, but combining sources risks exposing sensitive information. This topic introduces privacy-preserving record linkage (PPRL), techniques to match and merge records without compromising privacy. You'll learn why traditional methods are vulnerable and how PPRL uses cryptographic and statistical techniques to protect sensitive information during linkage. Pay attention to the balance between data utility and privacy in linkage scenarios.

Theory needs practical application in today's data environment. This topic provides hands-on skills with industry-standard tools and global frameworks for strong privacy and security. You'll apply real-world tools for de-identifying datasets, encrypting sensitive information, and securing data. This module shows how technology supports compliance and reduces risk. Gain awareness of global privacy frameworks, including ABS Five Safes and NIST Privacy Framework, understanding their role in data handling decisions. Focus on practical implementation and framework application.