Data Privacy Fundamentals: Understanding Privacy Risk and Deploying Privacy-Preserving Technologies

In the face of increasing regulation, rising consumer demand, and growing industry investment, data privacy is becoming a vital area within data engineering, data science, machine learning (ML), and artificial intelligence (AI). Whether you aim to work in advertising, healthcare, e-commerce, or Big Tech, understanding data privacy concepts and technologies is essential. This course will guide you through these critical discussions and introduce you to the burgeoning field of privacy engineering, focusing on some of the most prominent new technologies available.

This on-demand course covers best practices for tackling data privacy challenges using a hands-on marketing data-based use case. Through concise presentations and practical assessments, you’ll learn to navigate decisions around privacy risk and evaluate various privacy technologies. These include anonymization via differential privacy, federated learning and analytics, and secure data sharing through encrypted computation. By the end, you’ll grasp how privacy technologies can enhance data security and compliance for data teams and gain insights on managing organizational and legal conversations concerning sensitive data use.

What you’ll learn and how to apply it

By the end of this course, the learner should be able:

• Identify, evaluate, and assess sensitive data use risks and accompanying privacy risk in data projects
• Have an understanding of leading privacy technologies
• Understand and utilize best practices when sharing sensitive data at an organization internally and externally

Module Learning Objectives

• Module 1: Explore the multidisciplinary field of privacy and how to organize work in an organization around privacy. Recognize definitions of sensitive and proprietary data to evaluate what types of data the course will address. Apply a hands-on use case through the learning path.
• Module 2: Recognize the concept of privacy engineering and engage with basic privacy techniques such as pseudonymization, anonymization, and differential privacy. Apply these techniques via a hands-on exercise.
• Module 3: Evaluate options when data should remain local to where it is created, such as on IoT devices, mobile phones, or distributed data systems. Familiarize yourself with concepts of federated learning and analytics via a hands-on use case.
• Module 4: Assess how privacy risk changes when sharing data across parts of the organization or with external organizations. Learn some basics of encrypted computation.
• Module 5: Evaluate how to build a risk framework and align that with internal decision-making processes. Apply this to a concrete example of evaluating and setting up a data processing agreement with a third party and reviewing potential internal federated governance initiatives.
• Module 6: Discover ways to align across multiple stakeholders, build privacy championships and literacy across the organization, and be informed on how to learn more about the topics covered in the course.

This course is for you because

• You work in a data-related role and want to understand conversations around privacy and compliance.
• You architect data, ML, or AI systems and want to ensure you are building a future-proof system.
• You are interested in privacy as a growing area of importance within data systems and society.
• You’ve heard about concepts like federated learning, secure data sharing and anonymization, but you’re not sure what they mean or how to use them.
• You work or would like to work with sensitive or proprietary data in highly regulated fields such as finance, health, government, and/or Big Tech.

Prerequisites

• Python experience or knowledge is useful for the hands-on video segments
• Some modules directly handle data science and machine learning, where it would be helpful to have an intermediate understanding of the technologies and practices
• Basic understanding of data governance principles

Next Steps

• Read Practical Data Privacy for a more in-depth overview of the topics
• Work through the GitHub notebooks in the book’s repository for a more in-depth and hands-on exploration of the technologies
• Pick a privacy technology you would like to explore further, using a recommended library to explore more