# Privacy

Evaluate the privacy of your data and synthesizer before sharing it with others.

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><a href="https://docs.sdv.dev/sdmetrics/metrics/privacy-metrics"><strong>Compare Real vs. Synthetic data</strong></a></td><td>Apply any of the privacy metrics from our SDMetrics library to compare the real and synthetic data. SDMetrics measures disclosure protection, distance to closest record, and more.</td><td><a href="https://docs.sdv.dev/sdmetrics/metrics/privacy-metrics">https://docs.sdv.dev/sdmetrics/metrics/privacy-metrics</a></td></tr><tr><td>❖ <a href="/pages/P0M8MJRXcb6pTArVKVGS"><strong>Empirical Differential Privacy</strong></a></td><td>Empirically calculate the amount of privacy that your synthesizer algorithm provides. Compare different synthesizer algorithms to see their tradeoffs.</td><td></td></tr></tbody></table>

{% hint style="info" %}
❖ **SDV Enterprise Bundle**. This feature is available as part of the **Differential Privacy Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [Differential Privacy Bundle](/sdv/explore/sdv-bundles/differential-privacy.md) page.
{% endhint %}


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