Privately Learning Subspaces

Understand

Private data analysis suffers a costly curse of dimensionality.

• However, the data often has an underlying low-dimensional structure.
• For example, when optimizing via gradient descent, the gradients often lie in or near a low-dimensional subspace.
• If that low-dimensional structure can be identified, then we can avoid paying (in terms of privacy or accuracy) for the high ambient dimension.