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  3. Data Privacy and Anonymization in Python

Exercise

Suppression of sensitive attributes

In this exercise, you will identify a sensitive PII variable from a dataset loaded as a DataFrame clients_df.

Then, you will apply attribute suppression to the SPII variable. Remember that this is the strongest type of anonymization because there is no way of recovering any information from the attribute.

For this exercise and the remainder of the course, pandas will be imported for you and aliased as pd.

Instructions 1/4

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Question

Explore the DataFrame loaded as clients_df in the interactive console. Which is the sensitive PII attribute?

Possible answers