SoK: Fully Homomorphic Encryption Compilers
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2021.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
9 papers.
- 2022Private and Reliable Neural Network Inferencecited by 6
- 2021SoK: Privacy-preserving Deep Learning with Homomorphic Encryptioncited by 2
- 2023FLIPS: Federated Learning using Intelligent Participant Selectioncited by 2
- 2024Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Trainingcited by 2
- 2024Inference Attacks: A Taxonomy, Survey, and Promising Directionscited by 2
- 2022Partially Oblivious Neural Network Inferencecited by 1
- 2026CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computationscited by 1
- 2023Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programmingcited by 0
- 2026HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptationcited by 0
- 2026HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation1 Sep 2026
- 2026CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations27 Jan 2026
- 2024Inference Attacks: A Taxonomy, Survey, and Promising Directions4 Jun 2024
- 2024Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training6 Mar 2024
- 2023FLIPS: Federated Learning using Intelligent Participant Selection7 Aug 2023
- 2023Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming17 Feb 2023
- 2022Private and Reliable Neural Network Inference27 Oct 2022
- 2022Partially Oblivious Neural Network Inference27 Oct 2022
- 2021SoK: Privacy-preserving Deep Learning with Homomorphic Encryption23 Dec 2021