Conditional Masking

Conditional masking allows you to set multiple logical conditions that alter the masking format of a masking column. Conditional masking can only be done when editing an existing masking format of a masking column in a masking policy.

Example 1: Protecting Sensitive Identifiers Across Diverse Geographic Regions

Learn how conditional masking can be used to mask unique personal identifiers based on country.

Problem

A large organization manages a database containing personal identifiers such as Social Security Numbers, National Insurance Numbers, and so on from individuals living in various countries. They are required by regulations and data protection laws to safeguard the sensitive information while having to maintain usability for authorized purposed. However, sharing this data for testing, development, or analysis poses significant privacy risks.

Solution

By utilizing Data Masking available in Data Safe, the organization is able to assign the appropriate masking formats to the personal identifiers to meet the privacy regulations. Using conditional masking allows for the masking format to change based on the country of residence listed in the database.

Consider the database contains the following information:

Table 7-1 Employee Personal Identifiers, Pre-Masking

Employee	Country	Identifier
Alice	US	987-65-4320
Bill	UK	BH 123654G
Carol	UK	AJ 763482K
Denise	US	798-66-4329

Following the implementation of a conditional masking format conditional on the country, the database may look something like the following:

Table 7-2 Employee Personal Identifiers, Post-Masking

Employee	Country	Identifier
Alice	US	674-58-2371
Bill	UK	PA 123456C
Carol	UK	AB 987654B
Denise	US	543-23-5431

Benefits

By implementing conditional masking formats, an organization:

• Prevents unauthorized access to sensitive personal identifiers.
• Complies with diverse regional privacy laws and data protection requirements.
• Preserves data integrity and usefulness for authorized activities, such as testing and analysis.
• Reduces the risk of data breaches and potential harm to individuals.
• Enables secure data sharing for collaboration and knowledge advancement.

Example 2: Protecting Sensitive Salary Data Across Different Employee Groups

Learn how conditional masking can be used to mask salary data based on an employee's role.

Problem

A company needs to analyze salary data to identify potential pay gaps between different employee groups, but is unable to share actual salary figures due to internal privacy concerns or competitive reasons.

Solution

By utilizing Data Masking available in Data Safe, the company is able to create a pseudonymized dataset suitable for salary disparity analysis. Using conditional masking allows for the original salary data to be masked by a random number in a specified range based on the employee group.

Consider the database contains the following information:

Table 7-3 Employee Salary Data, Pre-Masking

Employee	Job Category	Salary
Alice	Manager	90,000
Bill	Manager	88,000
Carol	Worker	72,000
Denise	Worker	57,000
Eddie	Worker	70,000
Frank	Worker	45,000
George	Assistant	45,000

Following the implementation of a conditional masking format conditional on the following:

• If job category is Manager, replace salary with a random number from 100000 through 150000.
• If job category is Worker, set salary to a fixed number (75000).
• Default is to preserve the existing value.

The database may look something like the following:

Table 7-4 Employee Salary Data, Post-Masking

Employee	Job Category	Salary
Alice	Manager	100,200
Bill	Manager	132,000
Carol	Worker	75,000
Denise	Worker	75,000
Eddie	Worker	75,000
Frank	Worker	75,000
George	Assistant	45,000

Benefits

By implementing conditional masking formats, the company:

• Protects individual employee salary information while enabling analysis of potential pay gaps between different job categories.
• Maintains data utility by creating a masked dataset that retains the statistical properties necessary for identifying salary disparity trends.
• Supports internal fairness by enabling data-driven decisions to promote fair compensation practices within the organization.