CISSP Asset Security Practice Question
A financial institution stores customer PII, including Social Security numbers (SSNs). Under privacy regulations, SSNs are considered sensitive PII. Which of the following techniques would best reduce the risk of re-identification while preserving the utility of the data for statistical analysis?
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
✓
Differential privacy by adding calibrated noise to the dataset
Differential privacy adds noise to query results to protect individual records while allowing aggregate analysis, balancing privacy and utility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Anonymization by removing all direct identifiers
Why it's wrong here
Removing direct identifiers like names and account numbers is insufficient because indirect identifiers, or quasi-identifiers such as age, ZIP code, and gender, can be combined with external datasets to re-identify individuals. This vulnerability, known as a linkage attack, means that even seemingly anonymous data can expose sensitive PII when correlated with publicly available information, failing to provide robust privacy protection.
- ✗
Encrypting the entire dataset at rest
Why it's wrong here
Encrypting the entire dataset at rest primarily protects data confidentiality against unauthorized access to storage media, ensuring that data remains unreadable if the storage device is compromised. However, once the data is decrypted for analysis or processing, it becomes vulnerable to re-identification attacks by authorized users or systems that have legitimate access to the decrypted information. Encryption alone does not address privacy risks associated with data utility or access by those who can decrypt it.
- ✓
Differential privacy by adding calibrated noise to the dataset
Why this is correct
Differential privacy offers a strong, mathematically provable guarantee of privacy by introducing carefully calibrated noise into the dataset or query results. This noise ensures that the presence or absence of any single individual's data point does not significantly alter the output, making it extremely difficult for an adversary to infer specific individual attributes, even with substantial auxiliary information. It allows for aggregate statistical analysis while rigorously protecting individual privacy against sophisticated re-identification attempts.
- ✗
Pseudonymization by replacing names with random identifiers
Why it's wrong here
Pseudonymization replaces direct identifiers with artificial, non-meaningful substitutes, creating a linkable but non-identifying record. While it reduces the immediate risk of direct identification, the mapping between pseudonyms and original identities can often be reversed if the key or additional correlating data becomes available. This technique does not provide the strong, irreversible protection against re-identification necessary for highly sensitive PII, as the linkability inherently retains a re-identification risk.
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Related to this question
Learn chapter
Security Governance and Principles
Key term
Security
Security in IT is the practice of protecting systems, networks, and data from unauthorized access, damage, or theft.
Key term
Risk
Risk is the possibility that an event or action will negatively affect an organization's ability to achieve its goals, often measured in terms of likelihood and impact.
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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.