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Pattern 3: Encrypting Location Data

This pattern demonstrates how to protect precise location information, like a street address, while preserving less sensitive geographic data (like a city or state) for regional analytics.

The Pattern​

For any object containing a physical address:

  1. Encrypt the fields that provide precise location, such as street and the full zip code.
  2. Extract and Preserve fields that are useful for analytics but do not identify an individual, such as city, state, country, and the zip_prefix (the first 3 digits of a US zip code).
  3. Delete the original, plaintext sensitive fields.

Implementation​

You can add this logic to the same mapping processor you've been building in the previous steps.

  1. Start with your Pipeline: Open the encrypt-pii.yaml file from Step 2.

  2. Inspect the maintained mapping: Review the selected fields, key lookup, and plaintext removal in the linked reference before adapting the processor.

    See the complete configuration for the maintained encryption mapping and its ciphertext fields.

    Note: Use the key variable names from the configuration you are adapting.

Verification​

When you run data through the updated pipeline, the billing_address object will now be protected. The street and zip fields will be gone, replaced by their encrypted versions and the safe zip_prefix. City and state are preserved for your analytics needs. This same pattern can be applied to a shipping_address or any other location-based object.