Privacy-preserving AI

Overview

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.

Syllabus

• Introduction to Privacy-Preserving AI
Importance of privacy in AI systems
Overview of privacy risks and challenges
Regulatory landscape: GDPR and other privacy laws
• Foundational Techniques of Privacy-Preserving AI
Differential Privacy
Concepts and definitions
Mechanisms and applications
Advantages and limitations
Federated Learning
Architecture and use cases
Privacy benefits and challenges
Homomorphic Encryption
Principles and types
Practical applications in AI
• Implementing Privacy-Enhancing Technologies (PETs)
Integrating PETs into AI workflows
Balancing data utility with privacy
Case studies and real-world examples
• Ensuring Compliance and Performance
Techniques for compliance with GDPR and other regulations
Maintaining AI performance while preserving privacy
Tools and frameworks for privacy-preserving AI
• Navigating Challenges in Privacy-Preserving AI
Computational overhead and efficiency considerations
Addressing data utility trade-offs
Ethical and responsible AI principles
• Practical Applications and Innovations
Innovative use cases in various industries
Future trends in privacy-preserving AI
• Course Wrap-Up
Review of key concepts and techniques
Strategies for implementing privacy-preserving AI systems
Final project: Designing a privacy-preserving AI system
• Additional Resources and Further Learning
Recommended readings and research papers
Online communities and forums for continuous learning

---

Taught by

Ed Freitas

---

Subjects

Information Security (InfoSec)