13. Privacy-Aware and Robust Machine Learning
Machine learning (ML) systems face growing concerns about data privacy and robustness as they become more prevalent in real-world applications. This chapter covers foundational concepts such as differential privacy and federated learning, along with adversarial threats to model integrity. Practical defense techniques, tools, and regulatory implications are also discussed, emphasizing the importance of ethical AI development in an increasingly data-driven world.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Privacy is essential when training models on sensitive data.
- Differential Privacy (DP) is a key framework for ensuring privacy in machine learning.
- Adversarial attacks pose significant threats to model integrity and require robust defense mechanisms.
Key Concepts
- -- Differential Privacy
- A mechanism to quantify the privacy guarantees of ML outputs, ensuring that the presence or absence of a single data point does not significantly affect the output.
- -- Federated Learning
- A decentralized approach to training ML models, where data remains local to clients and only model updates are shared with a central server.
- -- Adversarial Examples
- Inputs that have been slightly modified to mislead ML models, demonstrating vulnerabilities in model robustness.
Additional Learning Materials
Supplementary resources to enhance your learning experience.