Privacy-preserving AI
Take on the role of VP of Technology to address privacy challenges in AI systems. This course will teach you to implement Privacy Enhancing Technologies (PETs) like Federated Learning, Differential Privacy, and Homomorphic Encryption.
What you'll learn
Privacy is a growing concern in AI systems, especially as organizations process vast amounts of sensitive data. Failing to address privacy risks can lead to regulatory penalties, eroded trust, and missed opportunities for innovation. In this course, Privacy-preserving AI, you’ll learn to implement Privacy-enhancing Technologies (PETs) that balance data utility with privacy and compliance. First, you’ll explore the foundational techniques of privacy-preserving AI, including Differential Privacy, Federated Learning, and Homomorphic Encryption. Next, you’ll discover how to practically implement these technologies in real-world AI workflows, ensuring compliance with regulations like GDPR while maintaining performance. Finally, you’ll learn how to navigate the challenges of privacy-preserving AI, such as computational overhead and data utility trade-offs, while aligning with ethical AI principles. When you finish this course, you’ll have the skills and knowledge of privacy-preserving AI techniques needed to build secure, compliant, and trustworthy AI systems that drive innovation and maintain user confidence.
Table of contents
About the author
Eduardo is a technology enthusiast, software architect and customer success advocate. He's designed enterprise .NET solutions that extract, validate and automate critical business processes such as Accounts Payable and Mailroom solutions for all types of organizations. He's designed and supported production systems for global names such as Coca Cola, Enel, Pirelli, Fiat-Chrysler, Xerox and many others. He's a well-known specialist in the Enterprise Content Management market segment, specifically... more