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Use Case

Privay-protected Cloud Migration

Cloud technology is revolutionizing how data-driven organizations work with their data; however, moving data to the cloud expose them to privacy risks and data residency regulations. Our solutions help organizations overcome these challenges by providing a way to generate privacy-protected datasets that can then be uploaded to the cloud or by allowing them to train statistical or ML models without moving data to different cloud locations.

Let Data Scientist access high value privacy-protected data

Truthful De-Identification

Leverage the power of differential privacy and k-anonymity to privacy-protect data in the data lake.

 

Quantified Privacy Risk

Defining and measuring Privacy risk of the data lake to reduce company exposure.

Preserve Analytical Value

Privacy-protected data that can be used for data science.

 

Example

A large bank wanted to broaden access to its data lake without compromising data privacy, preserving the data’s analytical value, and at reasonable infrastructure costs. Current approaches to de-identify data did not fulfill the compliance requirements and business needs, which had led to several bank projects being stopped. The issue with techniques like masking, tokenization, and aggregation, was that they did not sufficiently protect the data without overly degrading data quality.