I work at Microsoft Research with the SEAL team on applications for Fully Homomorphic Encryption (FHE). Batched integer FHE schemes are great for machine learni

I work at Microsoft Research with the SEAL team on applications for Fully Homomorphic Encryption (FHE). Batched integer FHE schemes are great for machine learning. The regular data access patterns in neural networks make using the batching easy and the rather "wide" computations lead to good encryption parameters. Also a new scheme, CKKS, gives the ability to divide by certain plaintext constants, which helps control scaling in fixed point representations. This comes in exchange for results being approximate, which is not a problem in machine learning. Currently what FHE needs is better toolchain support. Selecting encodings and encryption parameters is hard, but a lot can be done with good language support and tooling. We're presenting some of our work on automatically selecting good data layouts for NNs on FHE at the PPML workshop in NIPS next weekend [1]. [1] https://ppml-workshop.github.io/ppml/ https://ppml-workshop.github.io/ppml/