CryptoNumerics breaks through Data Barriers by using a combination of Decentralized Differential Privacy, Decentralized Private Set Intersection and Decentralized Secure Machine Learning.

Decentralized Differential Privacy

Make data Differentially Private by applying random sampling, followed by optimal attribute generalization with configurable metrics of information loss. Protect PII in either a single dataset or across decentralized data aligned by CN-Fuse when augmenting either features or observations.

Decentralized Private Set Intersection

Securely identify the intersection or symmetric difference of elements across sensitive datasets without revealing anything else and without using a “trusted” third party. Align datasets for use in decentralized secure machine learning in CN-Insight when augmenting either features or observations.

Decentralized Secure Machine Learning

Build secure machine learning models across decentralized datasets without exposing or moving the data by using techniques from Secure Multiparty Computation. Retain control over the types and frequency of models trained on your data.