An employer wants to track how bonus compensations have changed over time to identify employee outliers and correct earning disparities.
This task must be performed without exposing the sensitive compensation data for any individual and must be reversible to identify the outlier.
Which Cloud Data Loss Prevention API technique should you use to accomplish this?
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A. B. C. D.B.
To accomplish the task of tracking bonus compensations over time without exposing the sensitive compensation data for any individual and keeping it reversible to identify the outliers, we can use the Cloud Data Loss Prevention (DLP) API technique called Generalization.
Generalization is a DLP API technique that replaces specific data values with a general value within a defined range. It can be used to mask or blur sensitive information such as dates, ages, and other numerical data. By generalizing the bonus compensations, the employer can track how bonuses have changed over time without exposing the sensitive compensation data for any individual employee.
This technique can also be reversed to identify the outliers. By keeping a record of the generalization rules, the employer can reverse the generalization process and identify the employees who received outlier bonus compensations. This helps to correct earning disparities and ensure that employees are being compensated fairly.
Redaction, another DLP API technique, is used to completely remove or obscure sensitive data. This technique is not appropriate for this task since the employer wants to track the bonus compensations over time and identify outliers, which requires some level of data visibility.
CryptoHashConfig and CryptoReplaceFfxFpeConfig are DLP API techniques used for data encryption and pseudonymization, respectively. These techniques are not appropriate for this task since they do not allow for the reversal of the process, which is necessary for identifying the outliers.