> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdmetrics/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/sdmetrics/data-metrics/privacy.md). # Privacy Privacy metrics broadly capture the safety that synthetic data can provide to you, especially in cases where you'd like to disclose the synthetic data rather than real data. It's important to note that safety can be defined in many ways, depending on what type of information is valuable to protect and the assumptions about how it may be leaked. Privacy metrics are based on statistical patterns across your real and synthetic datasets. You can decide whether divulging the pattern (via synthetic data) is worth it for your project. ## Browse Apply these metrics to evaluate the privacy of your data tables: * [DisclosureProtection](/sdmetrics/data-metrics/privacy/disclosureprotection.md), [DisclosureProtectionEstimate](/sdmetrics/data-metrics/privacy/disclosureprotectionestimate.md): Measure the risk of disclosing sensitive information about specific, sensitive columns in your dataset. * [DCROverfittingProtection](/sdmetrics/data-metrics/privacy/dcroverfittingprotection.md): Measure the distance between the real and synthetic data, ensuring that your synthetic data doesn't too closely match the real data (Overfitting refers to your synthesizer being overfit on the real data.) * [DCRBaselineProtection](/sdmetrics/data-metrics/privacy/dcrbaselineprotection.md): Measure the distance between the real and synthetic data, comparing it against random data as a baseline. (Random data provides the highest privacy.) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.sdv.dev/sdmetrics/data-metrics/privacy.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.