Practice Metrics For Privacy (13.6.1) - Privacy-Aware and Robust Machine Learning
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Metrics for Privacy

Practice - Metrics for Privacy

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does ε represent in the context of differential privacy?

💡 Hint: Think about how changes in data affect model behavior.

Question 2 Easy

What does δ indicate in differential privacy?

💡 Hint: Consider this as an allowance for some acceptable risk.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does a smaller ε indicate in differential privacy?

Weaker privacy
Stronger privacy
No effect on privacy

💡 Hint: Remember, less ε equals more privacy.

Question 2

True or False: Delta (δ) in differential privacy allows for a margin of error.

True
False

💡 Hint: Think about the margin of error in various settings.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a machine learning model that uses differential privacy with specific ε and δ values. Discuss the implications of the chosen values on model performance and privacy.

💡 Hint: Relate how the choice of ε and δ affects real user data.

Challenge 2 Hard

Analyze a dataset subjected to membership inference attacks. What strategies would you employ to assess and improve its resilience against these attacks?

💡 Hint: Consider using augmented data obfuscation techniques.

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