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:

Anonymization

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.

Check out the given illustration on Anonymization transform.

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.

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

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.

Check out the given illustration on how to use Data Hashing transform.

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.

Steps to perform the Data Hashing transform:

Navigate to a 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.

Check out the given walk-through on the Data Masking transform.

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.

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 data variance to Numeric and Date columns.

Check out the given illustration on how to use Data Variance.

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 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.
Click the Submit option.

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.