> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-center-1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-center-1/data-center/data-preparation/data-preparation-workspace/transforms/anonymization.md).
# Anonymization
Anonymization is a type of information sanitization whose intent is privacy protection. It is a data processing technique that removes or modifies personally identifiable information.
The below-mentioned transforms are available under the Dates category:
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## **Data Hashing**
Data Hashing is a technique of using an algorithm to map data of any size to a fixed length. Every hash value is unique.
The Data Hashing is a data transformation technique used to convert raw data into a fixed-length representation in the form of a hash value. This transformation is often employed as part of the data preprocessing stage before using the data for various purposes such as analysis, machine learning, or storage. The main objective of data hashing as a data transform is to provide a more efficient and secure way to handle and process sensitive or large datasets.
{% hint style="info" %}
*Please Note:* A suitable hashing algorithm is chosen based on the specific requirements and security considerations as ***Hash Options***. *The supported Hash options are **Hash**, **Sha-1**, **Sha-2** and **MD-5**.*
{% endhint %}
* Navigate to the dataset within the Data Preparation framework, and select a column.
* Open the **Transforms** tab.
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select the required ***Hash Option.*** The supported Data Hashing options are Hash, Sha-1, Sha-2, MD-5.
* Click the ***Submit*** option.
* The selected column gets converted based on the hashing option (In the below-given case, the selected Data Hashing option is Hash).
Data Hashing with Sha1 Hash Option
* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***Sha1*** as Hash Option.
* Click the ***Submit*** option.
* The selected column gets converted based on the hashing option.
Data Hashing with Sha2 Hash Option
* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***Sha2*** as Hash Option.
* Select a Hash Value from the drop-down (The supported values are 256, 384, and 512).
* Click the ***Submit*** option.
* The selected column gets converted based on the hashing option.
Data Hashing with MD5 Hash Option
* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***MD5*** as Hash Option.
* Click the ***Submit*** option.
* The selected column gets converted based on the hashing option.
## **Data Masking**
Data masking transform is the process of hiding original data with modified content. It is a method of creating a structurally similar but inauthentic version of an actual **data.**
* Select a column within the Data Preparation framework.
* Open the ***Transforms*** tab.
* Select the ***Data Masking*** transform from the ***ANONYMIZATION*** category.
* Provide the ***Start Index*** and ***End Index*** to mask the selected data.
* Click the ***Submit*** option.
* The below-given image displays how the ***Data Masking*** transform (when applied to the selected dataset) converts the selected data:
## **Data Variance**
**The Data Variance transform allows the users to apply**
* Select the ***Data Variance*** transform from the ***Transforms*** tab.
* Select a column from data grid for transformation.
* Select the required Value Type-***Numeric/Date***.
* Configure the adequate information based on the Value Type.
* Click the ***Submit*** option.
* The data of the selected column gets modified based on the set.
### Applying the Data Variance transform to a Number Column
* Select a numeric column within the Data Preparation framework.
* Open the ***Transforms*** tab.
* Select the ***Data Variance*** transform from the ***ANONYMIZATION*** category.
* Select ***Numeric*** as the ***Value Type***.
* Configure the following details:
* Select an Operator using the drop-down option.
* Set percentage.
* Click the ***Submit*** option.
Applying the Data Variance Transform to a Numeric column
* The data of the selected column gets transformed based on the set numeric values.
### Applying the Data Variance transform to a Date Column
* Select a column containing Date values from the given dataset within the Data Preparation framework.
* Open the ***Transforms*** tab.
* Select the ***Data Variance*** transform from the ***ANONYMIZATION*** category.
* Select ***Date*** as the ***Value Type***.
* Provide the following details:
* Start Date
* End Date
* Click the ***Submit*** option.
Applying the Data Variance Transform to a Date column
* The selected Date column will display random dates from the selected date range.
{% hint style="info" %}
*Please Note: The **Data Variance** transform also provides space to add description while configuring the transformation information.*
{% endhint %}
