MAD: Memory-Aware Design Techniques for Accelerating Fully Homomorphic Encryption
Rashmi Agrawal, Leo de Castro, Chiraag Juvekar, Anantha P. Chandrakasan, Vinod Vaikuntanathan, Ajay Joshi
Abstract
Cloud computing has made it easier for individuals and companies to get access to large compute and memory resources. However, it has also raised privacy concerns about the data that users share with the remote cloud servers. Fully homomorphic encryption (FHE) offers a solution to this problem by enabling computations over encrypted data. Unfortunately, all known constructions of FHE require a noise term for security, and this noise grows during computation. To perform unlimited computations on the encrypted data, we need to perform a periodic noise reduction step known as bootstrapping. This bootstrapping operation is memory-bound as it requires several GBs of data. This leads to orders of magnitude increase in the time required for operating on encrypted data as compared to unencrypted data.