Privacy + Secure Machine Learning
Break the Data Barrier
Protect Privacy
Optimal k-Anonymity and Differential Privacy
Generate Insights
Decentralized Secure AI, ML
Control Intellectual Property
Never Expose Data
Eliminate “Trusted” Third Parties
Secure Analytics
Satisfy Regulatory Constraints
Never Move Data
“The world’s most valuable resource is no longer oil, but data.”
– The Economist
Every organization inevitably runs into the Data Barrier, the point at which valuable data is inaccessible due to risk of exposing sensitive information, risk of intellectual property loss, or regulatory constraints.
CryptoNumerics breaks the Data Barrier to generate better insights. This is achieved using a combination of Private Set Intersection, Differential Privacy, Optimal k-Anonymity, and federated or decentralized concurrent Secure Machine Learning.
Breaking through the Data Barrier
Financial Services
Investors work with data scientists to understand and attribute the performance and risk characteristics of their entire portfolio including their holdings in hedge funds.
Life Sciences
Researchers and data scientists increase the statistical significance and precision of their findings, ultimately advancing the development of new and improved therapies for cancer patients.
Social Good
Policy makers work with data scientists to develop better policies through increased understanding of issues in low-income areas for improved health outcomes and allocation of resources.
CryptoNumerics Team
We are a team of engineers and visionaries with previous experience in C-Level roles at Fortune 500 companies, in R&D roles at global organizations, and as founding teams of successful venture-backed start-ups.
Monica Holboke
Co-Founder & CEO/CSO
Ash Munshi
Co-Founder & Executive Chairman
Charles Marker
CTO
David Jensen
Head of Research
Peter Scholl
Cryptography Expert
Jimmy Fan
Co-Founder, Engineering
Roberto Cervantes
Co-Founder, Marketing
Hassan Bhatti
Co-Founder, Commercial