CDPSE Privacy Engineering Practice Question
When designing a system with k-anonymity, which metric measures the impact of generalization on the utility of the data?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Discernibility penalty
Discernibility penalty measures how much the data has been degraded to achieve the k-anonymity requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Epsilon value
Why it's wrong here
Epsilon is for differential privacy.
- ✗
Entropy loss
Why it's wrong here
This is not a standard k-anonymity metric.
- ✓
Discernibility penalty
Why this is correct
It quantifies the loss of utility due to generalization.
- ✗
F-score
Why it's wrong here
This is for machine learning model evaluation.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This CDPSE practice question is part of Courseiva's free ISACA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CDPSE exam.