Privacy compliance in a participant management system

WHAT THIS TESTS This evaluates privacy-by-design thinking and the messy reality that personal data sprawls across systems. The interviewer wants concrete mechanisms for consent, anonymization, and deletion that hold up to an audit, not a single delete statement.

A GOOD ANSWER COVERS For consent, store immutable, timestamped records capturing what the participant agreed to and which policy version, so you can prove consent and re-consent when terms change; gate data processing on the current consent state. For data handling, classify fields, separate personally identifiable information from research data, and apply data minimization, collecting only what the study needs. Use pseudonymization, replacing identifiers with tokens kept in a separate, access-controlled mapping, so research data stays usable while identity is protected; true anonymization removes the link entirely. For deletion requests, build a workflow that finds and removes or anonymizes the participant's data across all stores: primary database, caches, search indexes, logs, analytics, backups, and any third-party processors, then records proof of completion. Add access controls, encryption at rest and in transit, retention policies that auto-expire data, and an audit log of access and deletions.

COMMON WRONG ANSWERS Treating deletion as one DELETE row, ignoring backups, logs, caches, and external processors. No consent versioning, so you cannot prove what was agreed. Storing raw PII alongside research data with no separation. Skipping audit trails. Confusing pseudonymization with full anonymization.

LIKELY FOLLOW-UPS How do you handle deletion in immutable backups. What is the difference between anonymization and pseudonymization. How do you propagate a deletion to third-party data processors.

ONE CONCRETE EXAMPLE A participant requests deletion. Your workflow looks up their pseudonym mapping, deletes PII from the primary store, purges them from the search index and caches, scrubs identifying fields from logs, and notifies the payment processor to delete their records, recording each step. Backups are immutable, so you flag the record for exclusion on restore and rely on backup retention expiry, while the anonymized research responses, now unlinkable to any person, remain for valid analysis, satisfying both the deletion right and research continuity.