K-map is very similar to k-anonymity, except that it assumes that the attacker most likely doesn't know who is in the dataset. Use k-map if your dataset is relatively small, or if the level of effort involved in generalizing attributes would be too high.
Just like k-anonymity, k-map requires you to determine which columns of your database are quasi-identifiers. In doing this, you are stating what data an attacker will most likely use to re-identify subjects. In addition, computing a k-map value requires a re-identification dataset: a larger table with which to compare rows in the original dataset.
This topic demonstrates how to compute k-map values for a dataset using Sensitive Data Protection. For more information about k-map or risk analysis in general, see the risk analysis concept topic before continuing on.
Before you begin
Before continuing, be sure you've done the following:
- Sign in to your Google Account.
- In the Google Cloud console, on the project selector page, select or create a Google Cloud project. Go to the project selector
- Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project.
- Enable Sensitive Data Protection. Enable Sensitive Data Protection
- Select a BigQuery dataset to analyze. Sensitive Data Protection estimates the k-map metric by scanning a BigQuery table.