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Differentially Private and Secure Machine Learning
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Differentially Private and Secure Machine Learning
CryptoNumerics enables organizations, to share, lease (not sell) and collaborate without fear. Large, heterogeneous, and de-identified datasets can be combined into a global machine learning problem where data remains private to each owner and re-identification risk is minimized. The technology combines cryptography and numerical methods.

Our novel distributed private convex optimization software allows us to implement many of the most well known machine learning algorithms in a secure paradigm. This includes linear regression, SVM, collaborative filtering using alternating least squares, ridge regression, L1 filtering, and others.
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Monica Holboke
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Founder at CryptoNumerics
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