Advanced masking options extends the capability of data protection rules and data location rules by protecting the data with advanced de-identification techniques. The techniques maintain the format and integrity of the data. The high data utility affords data users, such as data scientists, business analysts, and application developers to produce high-quality insights from protected data.

Advanced masking options includes the following features:

• Format-preserving de-identification for 165 pre-defined data classes to maintain utility for AI projects.

• Relationship integrity to protect data consistently across related data sources.

The following scenarios explain how advanced masking option extends the capability of data protection rules.

Data scientists want to use financial data, such as credit card numbers and banking account numbers in their Machine Learning model to predict fraudulent transactions. The credit card numbers cannot be XXXXXXXXX to produce the results that they’re looking for. Instead, they need actual credit card numbers. The preserve format method in advanced masking opions produce credit card numbers that meet format requirements. Format requirements include maintaining issue identifier information (specifying which credit card company (Visa, Mastercard, and so on) issued the card), luhn checksum algorithm, and so on. The realistic masking ensures that data users can produce accurate results.

Healthcare data users want to use patient data that contains the patients' name and address information to analyze results from terminal disease clinical studies. The patient's name cannot be masked by "XXXX" to produce the results that they’re looking for. Instead, they need realistic names and realistic street names, cities, and countries. As a result, when data users are performing the analyses, they have a broader context that "Jane Doe" who lives on "123 Maple Lane" is the study participant with breast cancer.