Weekly Digs #3

Big news this week with a good mix of everything: guides to help you explore, practical tools, and interesting new ideas! Enjoy.

News

• The 2018 Gödel Prize is awarded to Oded Regev for his paper On lattices, learning with errors, random linear codes, and cryptography. This had a huge influence on later work in cryptography, not least homomorphic encryption. Via @hoonoseme.
• OpenMined is now maintaining a list of papers and tools around private machine learning: https://github.com/OpenMined/awesome-ai-privacy! Via @iamtrask.
• Lab41 has released a Python wrapper around Microsoft’s SEAL homomorphic encryption library: https://github.com/Lab41/PySEAL. Via @mortendahlcs.
• The list of accepted contributed talks for this year’s Theory and Practice of MPC workshop has been announced. This is the definitive annual event dedicated to secure multi-party computation. Via @claudiorlandi.

Papers

• Generating Differentially Private Datasets Using GANs
  Interesting idea of using GANs to produce artificial differential privacy-preserving datasets from sensitive data that are safe to release for further training purposes. This is done on the client side, meaning there’s no need for a trusted aggregator.
• Faster Homomorphic Linear Transformations in HElib
  The masters are at it again, giving algorithmic improvements to perhaps the most well-known homomorphic encryption library and thereby making it 30-75 times faster.
• Logistic Regression Model Training based on the Approximate Homomorphic Encryption
  Private fitting of several logisictic regression models on smaller genomic data sets using the HEAAN homomorphic encryption scheme. Approach is somewhat typical gradient descent and sigmoid polynomial approximation but with significant concrete performance improvements over other work using HEAAN.

Blogs

• The Building Blocks of Interpretability
  Nothing to do with private machine learning, yet this is so neat that it warrants a mention. Go play!