# FHEBench: Benchmarking Fully Homomorphic Encryption Schemes

arXiv: https://arxiv.org/abs/2203.00728 · HTML: https://mlsanity.com/paper/2203.00728/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Technical Evaluation of a Disruptive Approach in Homomorphic AI](https://mlsanity.com/paper/2506.11954/) (2025)
- [EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inference](https://mlsanity.com/paper/2511.00737/) (2025)

## Abstract (arXiv)

Fully Homomorphic Encryption (FHE) emerges one of the most promising solutions to privacy-preserving computing in an untrusted cloud. FHE can be implemented by various schemes, each of which has distinctive advantages, i.e., some are good at arithmetic operations, while others are efficient when implementing Boolean logic operations. Therefore, it is difficult for even cryptography experts let alone average users to choose the "right" FHE scheme to efficiently implement a specific application. Prior work only qualitatively compares few FHE schemes. In this paper, we present an empirical study, FHEBench, to quantitatively compare major FHE schemes