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Use Case

Privay-protected Data Lakes

Data Lakes are becoming more common among data-driven organizations in their search to extract value from their data assets. What these organizations are not aware of, is that their data lakes are privacy time bombs due to the presence of Personally Identifiable Information(PII). Our solutions classify and de-identify private information before it is sent into a data lake, effectively removing PII, thus creating data-lakes that are privacy-protected.

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.