Overview
Master privacy-enhancing technologies for AI systems, from differential privacy to federated learning and homomorphic encryption, to build secure and compliant solutions that protect sensitive data.
Syllabus
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- 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
Tags
On-Demand
