CISSP Asset Security Practice Question
A data warehouse contains anonymized customer transaction data used for analytics. The anonymization process removed direct identifiers and applied k-anonymity with k=10. An attacker obtains the dataset and attempts to re-identify individuals using auxiliary information. Which of the following best describes the residual privacy risk?
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
✓
Moderate risk because k-anonymity does not protect against attribute disclosure if the group is homogeneous
k-anonymity means each record is indistinguishable from at least k-1 other records, but attacks like homogeneity or background knowledge can still lead to re-identification, especially if auxiliary data is available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
No risk because anonymization eliminates all PII
Why it's wrong here
Anonymization, while reducing direct identifiers, does not eliminate all Personally Identifiable Information (PII) or the risk of re-identification. Techniques like k-anonymity protect against direct linkage but remain vulnerable to sophisticated inference or linkage attacks using auxiliary datasets, where quasi-identifiers can still lead to re-identification. Therefore, a residual risk always persists, meaning 'no risk' is an overstatement.
- ✗
High risk because k=10 is too small to provide meaningful privacy
Why it's wrong here
Stating k=10 is inherently 'too small' to provide meaningful privacy is an oversimplification. The adequacy of a k-value depends contextually on the dataset's characteristics, the granularity of quasi-identifiers, and the attacker's background knowledge. While a larger k generally offers more robust protection, the primary vulnerability in k-anonymity is often attribute disclosure due to homogeneity, rather than merely the numerical value of k itself.
- ✗
Low risk because k=10 ensures a group of at least 10 individuals
Why it's wrong here
While k-anonymity with k=10 indeed ensures that each individual's quasi-identifiers are indistinguishable from at least nine other records, this alone does not guarantee low risk. Attackers can still exploit external datasets to link records or infer sensitive attributes if the anonymous group is homogeneous. Therefore, the protection offered by k=10 is limited and does not inherently lead to a low-risk scenario against all attack vectors.
- ✓
Moderate risk because k-anonymity does not protect against attribute disclosure if the group is homogeneous
Why this is correct
This option correctly identifies a fundamental limitation of k-anonymity, known as the homogeneity attack. If all individuals within an equivalence class (a group of k records sharing identical quasi-identifiers) also share the same value for a sensitive attribute, then that attribute is effectively disclosed for everyone in the group. Despite the anonymity of individual identity, the sensitive information becomes known, leading to attribute disclosure and a moderate level of risk.
Go deeper
Related to this question
Learn chapter
Asset Security: Privacy and Data Retention
Key term
Identification
Identification is the process where a user or device claims an identity, often by providing a username, ID number, or account name, before proving that claim with authentication.
Key term
Anonymization
Anonymization is the process of removing or altering personally identifiable information so that an individual cannot be identified, directly or indirectly, from the remaining data.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CISSP practice question is part of Courseiva's free ISC2 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 CISSP exam.