CDPSE - Certified Data Privacy Solutions Engineer Technical Privacy Controls Questions and Answers

Question 1

A research institute needs to analyze medical data to identify disease trends. For the study's integrity, the data must remain linkable to individuals for longitudinal analysis, but direct identifiers must be removed to protect patient privacy. However, a mechanism must exist to re-identify a patient if a critical, life-threatening condition is discovered. Which technical privacy control is MOST appropriate?

Accepted Answer

Pseudonymization

Answer

Pseudonymization is the correct control because it replaces direct identifiers (like name or MRN) with a consistent but artificial identifier (a pseudonym). This allows for tracking and linking data for a specific individual over time without revealing their real-world identity. Crucially, the key linking the pseudonym back to the direct identifier is kept separate and secure, allowing for re-identification under strictly controlled and authorized conditions, such as a medical emergency.

Question 2

A financial services company wants to provide its development team with a realistic dataset for testing a new application. The dataset must mirror the production database's format and referential integrity but must not contain any actual customer Personally Identifiable Information (PII). Which of the following is the BEST technique to create this test dataset?

Accepted Answer

Static Data Masking

Answer

Static Data Masking (SDM) is the most appropriate technique. SDM creates a sanitized copy of a database by overwriting sensitive data with fictitious yet realistic-looking data. This process is done once, creating a new, safe dataset for non-production environments like development and testing. Dynamic Data Masking applies masking in real-time to live data, which is not suitable for creating a separate test database. Encryption protects data but does not alter it for testing purposes, and access control only limits who can see the data, it doesn't de-identify it.

Question 3

An organization needs to outsource complex data analytics to a third-party cloud service. A strict organizational policy requires that the sensitive data must remain encrypted at all times, including while the third party is actively performing computations on it. Which privacy-enhancing technology would enable this requirement?

Accepted Answer

Homomorphic Encryption

Answer

Homomorphic encryption is a specialized cryptographic method that allows computations to be performed directly on encrypted data (ciphertext) without needing to decrypt it first. The encrypted result, when decrypted, is identical to the result of the operations performed on the plaintext. This uniquely meets the requirement of processing data while it remains encrypted. TLS encrypts data in transit, and AES encrypts data at rest, but both require decryption before computation. Hashing is a one-way function not suitable for this type of analysis.

Question 4

A privacy engineer is configuring a Data Loss Prevention (DLP) system to detect and block emails containing U.S. Social Security Numbers (SSNs), which follow a specific `###-##-####` format. Which technical method is MOST commonly used within a DLP solution to identify structured data patterns like this?

Accepted Answer

Regular expression (RegEx) matching

Answer

Regular expression (RegEx) matching is the standard and most effective method for identifying specific, predictable patterns in text, such as the format of a Social Security Number, credit card number, or phone number. DLP systems use RegEx rules to scan content for sequences of characters that match these predefined patterns, allowing for precise detection of structured sensitive data.

Question 5

A social media platform wants to publish a report on user behavior trends without revealing information about any single individual. To accomplish this, their privacy engineering team implements a system that adds a precisely calculated amount of statistical noise to the aggregate query results before publication. This ensures that the presence or absence of any single user's data does not significantly affect the final output. This technique is known as:

Accepted Answer

Differential Privacy

Answer

Differential Privacy is a mathematically rigorous framework designed to share aggregate information about a dataset while withholding information about the specific individuals within it. It achieves this by injecting carefully calibrated statistical noise into the results of database queries, providing a formal guarantee that an adversary cannot confidently determine whether any given individual's data was included in the computation.

CDPSE - Certified Data Privacy Solutions Engineer Practice Test

CDPSE - Certified Data Privacy Solutions Engineer Practice Test

CDPSE - Certified Data Privacy Solutions Engineer Technical Privacy Controls Questions and Answers