In today’s world, access to large amounts of data is considered a valuable asset, especially within the context of building AI systems. However, having such access may run counter to fundamental privacy principles. Privacy regulatory regimes globally recognize that data minimization is imperative to reducing privacy risks. There are a number of data minimization practices such as collection, storage, disclosure, and the linkability of data that often falls within the purview of privacy engineers. Privacy engineers are usually tasked with turning privacy requirements into technical realities. They suggest various tools, techniques and best practices to embed privacy into the design of systems, processes and practices to ensure that privacy standards are consistently applied across a company’s products and services, often in the face of increasing regulatory requirements. Data minimization is essential to ensure that personal information is appropriately safeguarded and accordingly, the demand for privacy engineering will only increase.

What you will learn:

• An understanding of the practice of data minimization — including but not limited to data classification, inventory, mapping and lineage.
• Tools and techniques used to implement data minimization, such as differential privacy, anonymization, pseudonymization and redaction.
• An understanding of the statutory requirements related to data minimization — including GDPR, PIPEDA/Bill C-27 and CCPA/CPRA.