Your retail organization needs to share a daily customer-transaction table with an external machine-learning vendor. The vendor must be able to join transactions that belong to the same shopper across multiple days, but must never receive personally identifiable information such as names, emails or card numbers. Which data-sharing technique best meets both the privacy and analytical requirements?
Remove all identifying columns and aggregate the data to store totals per store per day.
Mask sensitive fields by replacing the middle characters of names and card numbers with asterisks.
Replace each email address with a deterministic one-way hash before sending the file.
Encrypt the entire dataset with AES-256 and email the decryption key to the vendor.
Using a deterministic one-way hash (or similar tokenization approach) on customer identifiers converts each value into an irreversible surrogate that is identical every time the same input appears. This keeps PII out of the file while still allowing the vendor to join rows that refer to the same shopper. Encrypting the entire file and then handing over the key simply reveals the raw PII once it is decrypted. Aggregating the data removes the ability to link individual shoppers across days, preventing the required analyses. Partially masking names or card numbers leaves enough of the original values to risk re-identification and still exposes other direct identifiers such as email addresses.
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CompTIA Data+ DA0-002 (V2)
Data Governance
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