Practice Robust and Private Model Evaluation - 13.6 | 13. Privacy-Aware and Robust Machine Learning | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does Ξ΅ represent in differential privacy?

πŸ’‘ Hint: Think about the level of information exposure.

Question 2

Easy

What is empirical attack success rate?

πŸ’‘ Hint: Consider how attackers might test their hypotheses.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does a smaller Ξ΅ indicate in terms of model privacy?

  • Stronger privacy
  • Weaker privacy
  • No effect on privacy

πŸ’‘ Hint: Think about the relationship between Ξ΅ and privacy.

Question 2

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

  • True
  • False

πŸ’‘ Hint: Consider what success means for attackers.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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

πŸ’‘ Hint: Consider real-world applications while designing.

Question 2

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

πŸ’‘ Hint: Think about the methods we discussed on boosting robustness.

Challenge and get performance evaluation