# SoK: Fully Homomorphic Encryption Compilers

arXiv: https://arxiv.org/abs/2101.07078 · HTML: https://mlsanity.com/paper/2101.07078/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2021.

## Built on by (most cited first, 9 of 9)

- [Private and Reliable Neural Network Inference](https://mlsanity.com/paper/2210.15614/) (2022)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [FLIPS: Federated Learning using Intelligent Participant Selection](https://mlsanity.com/paper/2308.03901/) (2023)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [Inference Attacks: A Taxonomy, Survey, and Promising Directions](https://mlsanity.com/paper/2406.02027/) (2024)
- [Partially Oblivious Neural Network Inference](https://mlsanity.com/paper/2210.15189/) (2022)
- [CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations](https://mlsanity.com/paper/2601.19367/) (2026)
- [Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming](https://mlsanity.com/paper/2302.08930/) (2023)
- [HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation](https://mlsanity.com/paper/2609.01730/) (2026)

## Abstract (arXiv)

Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009. The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development. In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools' performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.