> For the complete documentation index, see [llms.txt](https://help.protecto.ai/getting-started/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.protecto.ai/getting-started/protecto-overview/introduction/protecto-vault/tokenization-apis/masking/mask-with-format-and-token.md).

# Mask with format and token

This technique masks data based on user-specified token types (e.g., Text token, Alphanumeric, Numeric Token etc.) as well as predefined formats (e.g., Phone, Email etc.).

***Note:*** To send a request with the authentication token, include the following in headers: 

Headers: {"Authorization": "Bearer \<AUTH\_TOKEN>"}

<figure><img src="https://323347149-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fw6GKvSvsZfGhtiQWrONh%2Fuploads%2FuDmIkNLQh54JL2kQDSHs%2Fimage.png?alt=media&amp;token=27997f35-4c8d-435f-aa53-898979b9128f" alt=""><figcaption></figcaption></figure>

**`Endpoint:`**

```
https://protecto.ai/api/vault/mask
```

**`Request Method: PUT`**

**`Request Payload:`**

```
{"mask": [{ "value": “George Williams”,”token_name": "Numeric Token","format": "Person Name" }]}


```

**`Response:`**

```
{"data": [{"value": " George Williams “, "token_value”": “243 3466",” token_name": "Numeric Token “,"format": "Person Name" }]}
```

**`Response Parameters:`**

·       value (string): Original sensitive data value.

·       token\_value (string): Generated token replacing the sensitive data.

·       format (string): Predefined format of the sensitive data.

**`Advantage:`**

This approach involves replacing sensitive information with tokens while adhering to specific formats that are relevant to the type of data.

1. **`Context Preservation:`** By retaining certain parts of the format, users can still recognize the type of data (e.g. email address) even after masking.
2. **`User Identification:`** Users can quickly identify the masked data (e.g. email address) due to the retained format elements.
3. **`Data Security:`** Sensitive information, such as domain and top-level domain, is masked while allowing users to understand the context of the data.
4. **`Usability:`** Masked data remains usable for identification and analysis purposes, even though specific sensitive details are protected.
5. **`Regulatory Compliance:`** The approach helps organizations comply with data protection regulations while ensuring that users can identify the type of masked data.