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Answer: Implement column-level security, restricting access to sensitive columns and providing masked views to unauthorized users.
The most efficient approach for implementing data masking at scale in a large-scale data lakehouse to ensure compliance with privacy regulations is to implement column-level security and provide masked views to unauthorized users. This method offers granular control over access to sensitive data, ensuring that only authorized users can view unmasked PII. Masked views allow unauthorized users to access necessary information without exposing actual PII. This strategy is scalable, efficient across multiple datasets, and supports compliance with regulations like GDPR, HIPAA, or CCPA, thereby reducing the risk of data breaches and non-compliance penalties.
Author: LeetQuiz Editorial Team
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For a large-scale data lakehouse with petabytes of data, what is the most efficient method to implement data masking for Personally Identifiable Information (PII) across multiple datasets to comply with privacy regulations?
A
Utilize an on-the-fly encryption model where data is only decrypted and masked as needed when accessed by unauthorized users.
B
Apply real-time data masking at the query level, using dynamic data masking capabilities of the lakehouse platform.
C
Implement column-level security, restricting access to sensitive columns and providing masked views to unauthorized users.
D
Perform batch data masking operations during off-peak hours, rewriting datasets with masked versions of sensitive fields.
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