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Answer: CryptoReplaceFfxFpeConfig
The question requires a reversible transformation that protects sensitive compensation data while allowing analysis to identify outliers. CryptoReplaceFfxFpeConfig (D) is the correct choice because it uses format-preserving encryption (FPE) to replace original values with encrypted tokens of the same format, making it reversible with the encryption key. This allows the employer to analyze pseudonymized data for trends and outliers, then re-identify specific employees when needed. Generalization (A) is not fully reversible as it replaces values with generalized ranges, losing precision. Redaction (B) removes data entirely, preventing analysis. CryptoHashConfig (C) is irreversible, as hashing cannot be reversed to the original value. The community discussion strongly supports D, with high upvotes and references to Google documentation confirming its reversibility for pseudonymization use cases.
Author: LeetQuiz Editorial Team
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An organization needs to analyze how bonus compensation has evolved over time to detect employee outliers and rectify earning disparities. The analysis must be conducted without exposing any individual's sensitive compensation data, but the process must be reversible to allow for the identification of the specific outliers.
Which Cloud Data Loss Prevention API technique should be used for this task?
A
Generalization
B
Redaction
C
CryptoHashConfig
D
CryptoReplaceFfxFpeConfig