*Re-identification risk analysis* , or just *risk analysis*, is the process of
analyzing sensitive data to find properties that might increase the risk of
subjects being identified. You can use risk analysis methods before
de-identification to help determine an effective de-identification strategy or
after de-identification to monitor for any changes or outliers.

Sensitive Data Protection can compute four re-identification risk metrics: *k* -anonymity,
*l* -diversity, *k* -map, and *δ* -presence. If you're not familiar with risk
analysis or these metrics, see the [risk analysis concept
topic](https://docs.cloud.google.com/sensitive-data-protection/docs/concepts-risk-analysis) before continuing on.

This section provides overviews of how to use Sensitive Data Protection for risk
analysis of structured data using any of these metrics, plus other related
topics.

## Calculate re-identification risk

Sensitive Data Protection can analyze your structured data stored in
BigQuery tables and compute the following re-identification risk
metrics. Click the link for the metric you want to calculate to learn more.

| Metric | Description |
|---|---|
| [*k*-anonymity](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-k-anonymity) | A property of a dataset that indicates the re-identifiability of its records. A dataset is *k* -anonymous if quasi-identifiers for each person in the dataset are identical to at least *k* -- 1 other people also in the dataset. |
| [*l*-diversity](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-l-diversity) | An extension of *k* -anonymity that additionally measures the diversity of sensitive values for each column in which they occur. A dataset has *l* -diversity if, for every set of rows with identical quasi-identifiers, there are at least *l* distinct values for each sensitive attribute. |
| [*k*-map](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-k-map) | Computes re-identifiability risk by comparing a given de-identified dataset of subjects with a larger re-identification---or "attack"---dataset. |
| [*δ*-presence](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-d-presence) | Estimates the probability that a given user in a larger population is present in the dataset. This is used when membership in the dataset is itself sensitive information. |

## Calculate other statistics

Sensitive Data Protection can also compute numerical and categorical
statistics for data stored in BigQuery tables using the same
[`DlpJob`](https://docs.cloud.google.com/sensitive-data-protection/docs/reference/rest/v2/projects.dlpJobs) resource as the
risk analysis APIs.

| Metric | Description |
|---|---|
| [Numerical statistics](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-stats#compute-num-stats) | Determines minimum, maximum, and quantile values for an individual BigQuery column. |
| [Categorical numerical statistics](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-stats#compute-cat-num-stats) | Computes categorical numerical statistics for the individual histogram buckets within a BigQuery column. |

For more information, see
[Computing numerical and categorical statistics](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-stats).

## Visualize re-identification risk

You can visualize the risk metrics that Sensitive Data Protection calculates
directly in the Google Cloud console using Sensitive Data Protection
([*k*-anonymity](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-k-anonymity#viewing-results) or
[*l*-diversity](https://docs.cloud.google.com/sensitive-data-protection/docs/compute-l-diversity#viewing-results)), or using other
Google Cloud products.

| Product | Description |
|---|---|
| [Data Studio](https://docs.cloud.google.com/sensitive-data-protection/docs/visualizing_re-id_risk) | After calculating [*k*-anonymity](https://docs.cloud.google.com/sensitive-data-protection/docs/concepts-risk-analysis#about-k-anonymity) values for a dataset using Sensitive Data Protection, you can visualize the results in Data Studio. By doing so, you'll also be able to better understand re-identification risk and help evaluate the trade-offs in utility you might be making if you redact or de-identify data. |