TL;DR: We allow analytics and ML to be applied on sensitive data, providing
mathematically guaranteed private outputs.
Differential privacy (DP) is a set of systems and practices that help keep the data of
individuals safe and private. Differential Privacy offers the strongest possible
privacy protection available today, with a mathematical proof to back up each
algorithm. Differential privacy is achieved by introducing statistical noise.
The noise is significant enough to protect the privacy of any individual in the
data, but small enough that it will not impact the accuracy of analytics and machine
learning methods applied on the data.
Read more about DP on our blog
PVML offers proprietary Differential Privacy
technology to extract useful insights and train AI models using datasets containing
sensitive information. Our algorithms are performed "backstage" so the outputs
returned to the analysts or trusted third-parties are private and ready to be used
Learn more about how we use DP