Tumult Analytics Features

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.

Provides users the ability to generate synthetic structured data.

Provides user the ability to generate synthetic image data.

Can provide accurate annotations for synthesized image data.

Allows user to gain insight and utility into the data.

Provides automated reports of the quality of the synthesized data.

Gives user the ability to remove sensitive data from your workflows without sacrificing data utility using techniques such as differential privacy.

Allows for the ingestion of a variety of data formats (e.g. CSV, Hadoop, etc.)

Has the ability to consume and synthesize large quantities of data effectively and efficiently.

Has capabilities for creating a completely new dataset with no actual data from original dataset.

Can synthesize part of a dataset, leaving in data which is not private or sensitive.