Weekly Digs #1

We are happy to kick off the weekly dig into what’s going on in private machine learning!

It’s an increasingly busy field so we’ll keep it short, with a mix of research papers, blog posts, and general news. Sometimes just a quick summary, sometimes a small commentary. Enjoy.

Papers

• The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
  Concrete study of what a model can leak about sensitive information in the training data. Perhaps not surprisingly, “only by developing and training a differential private model are we able to … protect against the extraction of secrets”.
• Doing Real Work with FHE: The Case of Logistic Regression
  The heavyweights of homomorphic encryption apply HElib to logistic regression with a focus on implementing “optimized versions of many bread and butter FHE tools. These tools include binary arithmetic, comparisons, partial sorting, and low-precision approximation of complicated functions such as reciprocals and logarithms”.
• Reading in the Dark: Classifying Encrypted Digits with Functional Encryption
  Develops a functional encryption scheme for “efficient computation of quadratic polynomials on encrypted vectors” and applies this to private MNIST prediction (i.e. using a model trained on unencrypted data) via suitable quadratic models.