Practice Robust And Private Model Evaluation (13.6) - Privacy-Aware and Robust Machine Learning
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Robust and Private Model Evaluation

Practice - Robust and Private Model Evaluation

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does ε represent in differential privacy?

💡 Hint: Think about the level of information exposure.

Question 2 Easy

What is empirical attack success rate?

💡 Hint: Consider how attackers might test their hypotheses.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does a smaller ε indicate in terms of model privacy?

Stronger privacy
Weaker privacy
No effect on privacy

💡 Hint: Think about the relationship between ε and privacy.

Question 2

True or False: Higher empirical attack success rates indicate a stronger privacy model.

True
False

💡 Hint: Consider what success means for attackers.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design an evaluation framework that balances ε and δ for a proposed ML model on sensitive data. Discuss the implications of your chosen values.

💡 Hint: Consider real-world applications while designing.

Challenge 2 Hard

Analyze a model with clean accuracy of 95% and robust accuracy of 60%. Discuss potential improvements to enhance robustness.

💡 Hint: Think about the methods we discussed on boosting robustness.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.