Nymiz Features

The ability to create fields that automatically mask data such as credit card numbers or passwords.

The ability to mask data in real time as it is entered.

The ability to apply or remove masking after data has been entered.

Tools to mask data using a consistent ruleset.

Tools to mask data using random characters and data.

Meets GDPR requirements for pseudonymisation under the Data Protection by Design and by Default requirements.

Meets de-identification requirements under the CCPA.

Offers traditional static de-identification (also known as consistent replacement), where the pseudonymized data uses the same pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox and the Robert Fox name is used multiple times. This type of pseudonymization carries some risks of re-identification if paired with enough datasets.

Offers dynamic de-identification (also known as random replacement), where the pseudonymized data uses different pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox once, and then the next time the data is used the name changes to Michael Jones. This type of pseudonymization carries lesser risk of re-identification if paired with many datasets.

Offers methods to de-identify large volumes of data using batch files.

Connects to mobile platforms using a mobile SDK.

Offers APIs to connect products.