# 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:

### **​**![](/files/iKGHXDeC22jxvm8nY9Yv) <a href="#undefined" id="undefined"></a>

## **Anonymization** <a href="#data-hashing" id="data-hashing"></a>

### **Hashing Anonymization (using Salt and Pepper technique)**

This transformation using the ***Salt and Pepper*** technique is a method to protect sensitive data by introducing random noise or fake data points into a dataset while preserving its statistical properties.

{% hint style="success" %}
*Check out the given illustration on Anonymization transform.*
{% endhint %}

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FIctE5LjGWDD6zEdW4vpJ%2Fuploads%2FDoPfd9BHaipHWVotZRuw%2FAnonymization_Anonymization.mp4?alt=media&token=8b1f7b0e-5572-48aa-b559-e13333c6d804>" %}
***Anonymization Transform***
{% endembed %}

Steps to perform the ***Anonymization*** Transform:

* Navigate to a dataset within the Data Preparation framework, and select a column.
* Select one column that needs to be protected.
* Select the ***Transforms*** tab.
* Select the ***Anonymization*** (Hashing Anonymization) transform from the ***Anonymization*** category.
* Pass the ***Set Values*** (pass any random data as numerical or string values)
* Select columns in the ***Set Fields*** which can be used in the transformation.
* Select a ***Hash Option*** using the drop-down menu.
* Click the ***Submit*** option.

<figure><img src="/files/16nXQ08hs8fCxOkIdcDA" alt=""><figcaption></figcaption></figure>

* The result will update on the selected column by protecting the data in a hashed format.

<figure><img src="/files/sq5hzYLihjdJHJu3tOq1" alt=""><figcaption></figcaption></figure>

## **Data Hashing** <a href="#data-hashing" id="data-hashing"></a>

Data Hashing is a technique of using an algorithm to map data of any size to a fixed length. Every hash value is unique.&#x20;

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="success" %}
*Check out the given illustration on how to use Data Hashing transform.*
{% endhint %}

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FIctE5LjGWDD6zEdW4vpJ%2Fuploads%2F9so0UiYXOznKo33c6cu5%2FAnonymization_DataHashing.mp4?alt=media&token=5eb7b960-9e8c-49bd-aef5-cce0c0cfe290>" %}
***Data Hashing***
{% endembed %}

{% hint style="info" %}
*<mark style="color:green;">Please Note</mark>:* 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**.*

![](/files/AE8jrUb45KoIFwOnglxM)
{% endhint %}

Steps to perform the ***Data Hashing*** transform:

* Navigate to a dataset within the Data ~~P~~reparation framework, and select a column.
* Open the **Transforms** tab.&#x20;
* 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.

<figure><img src="/files/50t4EkXrYoZIrKzxGgjl" alt=""><figcaption></figcaption></figure>

* The selected column gets converted based on the hashing option (In the below-given case, the selected Data Hashing option is Hash).

<figure><img src="/files/Bon81JFGbKxxf3kQmqik" alt=""><figcaption></figcaption></figure>

<details>

<summary>Data Hashing with Sha1 Hash Option</summary>

* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.&#x20;
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***Sha1*** as Hash Option.&#x20;
* Click the ***Submit*** option.

&#x20;      ![](/files/m2BKynegZJ0bl8VDneBh)

* The selected column gets converted based on the hashing option.

&#x20;      ![](/files/JTUwokqegxNCZ0NvrXXo)

</details>

<details>

<summary>Data Hashing with Sha2 Hash Option</summary>

* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.&#x20;
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***Sha2*** as Hash Option.&#x20;
* Select a Hash Value from the drop-down (The supported values are 256, 384, and 512).&#x20;
* Click the ***Submit*** option.

&#x20;      ![](/files/c94INAN8Ux1lSMeAPlbo)

* The selected column gets converted based on the hashing option.

&#x20;      ![](/files/R0OPP6InoTcAgp3XlM7Q)

</details>

<details>

<summary>Data Hashing with MD5 Hash Option</summary>

* Select a column from the given dataset within the ***Data Preparation*** framework.
* Open the ***Transforms*** tab.&#x20;
* Select the ***Data Hashing*** transform from the ***ANONYMIZATION*** category.
* Select a column from data grid for transformation.
* Select ***MD5*** as Hash Option.&#x20;
* Click the ***Submit*** option.

&#x20;     ![](/files/xToE4Eh1rvqCoH9qTr4J)

* The selected column gets converted based on the hashing option.

&#x20;      ![](/files/zta5sTMV6n4sNs3egKw7)

</details>

## **Data Masking** <a href="#data-masking" id="data-masking"></a>

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.**

{% hint style="success" %}
*Check out the given walk-through on the Data Masking transform.*
{% endhint %}

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FIctE5LjGWDD6zEdW4vpJ%2Fuploads%2FvRXDk58MQnngBVMMpnRq%2FAnonymization_DataMasking.mp4?alt=media&token=20ec12f4-70fb-42bb-940a-7f67d170219a>" %}
***Data Masking***
{% endembed %}

Steps to perform the ***Data Masking*** Transform:

* 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.

<figure><img src="/files/O08e3toGJNvgLkSaMbsX" alt=""><figcaption></figcaption></figure>

* The below-given image displays how the ***Data Masking*** transform (when applied to the selected dataset) converts the selected data:​

<figure><img src="/files/1Bto8MLrccgS1PKmbt5d" alt=""><figcaption></figcaption></figure>

## **Data Variance** <a href="#data-variance" id="data-variance"></a>

The Data Variance transform allows the users to apply data variance to Numeric and Date columns.

{% hint style="success" %}
*Check out the given illustration on how to use Data Variance.*
{% endhint %}

{% embed url="<https://files.gitbook.com/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FIctE5LjGWDD6zEdW4vpJ%2Fuploads%2FJ3aoKB3OH3eS2CydOzhH%2FAnonymization_DataVariance.mp4?alt=media&token=41e2c307-f440-4bdb-9ff6-16c2f31c3992>" %}
***Data Variance***
{% endembed %}

* Select the ***Data Variance*** transform from the ***Transforms*** tab.
* Select a column from data grid for transformation.
* Select the required Value Type-***Numeric/Date***.

<figure><img src="/files/rudyWqbtD4G2PzrxYISR" alt=""><figcaption></figcaption></figure>

* 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 value type.

### 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.&#x20;
* Click the ***Submit*** option.​

<figure><img src="/files/wmnW5GNmURlGsxVhd44e" alt=""><figcaption><p><em><strong>Applying the Data Variance Transform to a Numeric column</strong></em></p></figcaption></figure>

* The data of the selected column gets transformed based on the set numeric values.

<figure><img src="/files/wCH8mmYt98mCa4z7Vxyw" alt=""><figcaption></figcaption></figure>

### 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.

<figure><img src="/files/UhEXxYFYs2oRGjA9mJ3G" alt=""><figcaption><p><em><strong>Applying the Data Variance Transform to a Date column</strong></em></p></figcaption></figure>

* The selected Date column will display random dates from the selected date range.

<figure><img src="/files/rU8EH4UR4nHnUXKYiuMe" alt=""><figcaption></figcaption></figure>

{% hint style="info" %}
*<mark style="color:green;">Please Note</mark>: The **Data Variance** transform also provides space to add description while configuring the transformation information.*
{% endhint %}


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