Google Professional Machine Learning Engineer
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In your role at a leading Banking group, you're tasked with enhancing the security of customer data processing. The project involves the automated collection and processing of large datasets from various documents, which contain highly sensitive information such as credit card numbers and personal identification details. Due to strict compliance requirements, this sensitive data must not be disclosed in any form. Your team is considering several techniques to ensure data confidentiality by substituting sensitive data with surrogate characters, while maintaining the data's usability for analysis. Which of the following techniques would be most appropriate for this scenario, considering the need for data utility and compliance with data protection regulations? (Choose two options if option E is available)
Explanation:
Masking is the most suitable technique for this scenario as it directly replaces sensitive values with surrogate characters, ensuring the data's confidentiality without altering its format, which is crucial for maintaining data utility. Tokenization is also appropriate as it replaces sensitive data with non-sensitive tokens, further enhancing security without compromising the data's usability for analysis. Format-preserving encryption (FPE) encrypts data while maintaining its original format, but it may not be as straightforward as masking or tokenization for the specific requirement of surrogate character substitution. k-anonymity anonymizes data to prevent identification of individuals but doesn't specifically address the need for surrogate characters. Replacement substitutes sensitive data with specified values but lacks the specificity of masking or tokenization for character substitution and security enhancement.