The scenario involves a retailer using sensitive customer data (AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, SHIRT_SIZE) for training machine learning models. The question focuses on the best method to secure this data before providing it to the data science team.
Four options are presented:
The suggested solution is A, which advocates for tokenizing the data using hashed dummy values to replace real values, thereby protecting sensitive information while allowing model training.
You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?
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