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GCP Generative AI Leader Practice Question 10: Reversible Deidentification Privacy

Google Cloud Generative AI Leader · Test 1 · question 10 of 10

Business Strategies for Gen AIhard

A hospital network wants a data science partner to use gen AI to find patterns in patient records that predict readmission. The privacy officer has two conditions. The partner must never see names, medical record numbers, or other direct identifiers. However, when the analysis flags a high-risk case, the hospital's own care team must be able to trace it back to the right patient so they can intervene.

Which privacy approach meets both conditions?

Show answer and explanation

Answer:

B. Pseudonymize the identifiers with reversible tokens, and keep the key only with a restricted hospital team that re-identifies flagged cases

The two conditions pull in opposite directions. The partner must not be able to identify anyone, yet the hospital must be able to re-link flagged records. Pseudonymization with two-way tokens resolves this. Google Cloud's Sensitive Data Protection describes it as replacing sensitive values with cryptographically generated tokens. Reversible methods, such as deterministic encryption or format-preserving encryption, let whoever holds the key turn a token back into the original value. The partner receives tokens only. The hospital keeps the key under tight access control and re-identifies just the cases the model flags. The data stays useful for analysis, and exposure is kept to a minimum.

Option A meets the first condition and breaks the second. True anonymization is designed to be irreversible, so the care team could not find the patient behind a high-risk record.

Option C is irreversible in practice. Once the lookup table is thrown away, nobody can map the random values back to patients. That makes it effectively anonymization with the same flaw as A.

Option D relies only on a contract. The partner still receives the identifiers, so the privacy officer's first condition is not met. The privacy risk is managed on paper rather than removed from the data.

Reference: Pseudonymization

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