  Privacy concerns have thrust privacy-preserving computation into the
spotlight. Homomorphic encryption (HE) is a cryptographic system that enables
computation to occur directly on encrypted data, providing users with strong
privacy (and security) guarantees while using the same services they enjoy
today unprotected. While promising, HE has seen little adoption due to
extremely high computational overheads, rendering it impractical. Homomorphic
encryption (HE) is a cryptographic system that enables computation to occur
directly on encrypted data. In this paper we develop a benchmark suite, named
NTTSuite, to enable researchers to better address these overheads by studying
the primary source of HE's slowdown: the number theoretic transform (NTT).
NTTSuite constitutes seven unique NTT algorithms with support for CPUs (C++),
GPUs (CUDA), and custom hardware (Catapult HLS).In addition, we propose
optimizations to improve the performance of NTT running on FPGAs. We find our
implementation outperforms the state-of-the-art by 30%.
