# EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

arXiv: https://arxiv.org/abs/1912.11951 · HTML: https://mlsanity.com/paper/1912.11951/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture](https://mlsanity.com/paper/2106.00038/) (2021)
- [HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks](https://mlsanity.com/paper/2302.02407/) (2023)
- [Private and Reliable Neural Network Inference](https://mlsanity.com/paper/2210.15614/) (2022)
- [AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems](https://mlsanity.com/paper/2604.03425/) (2026)
- [FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data](https://mlsanity.com/paper/2102.03494/) (2021)
- [EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting](https://mlsanity.com/paper/2110.13638/) (2021)
- [HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models](https://mlsanity.com/paper/2302.08260/) (2023)
- [Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference](https://mlsanity.com/paper/2402.01296/) (2024)
- [PackVFL: Efficient HE Packing for Vertical Federated Learning](https://mlsanity.com/paper/2405.00482/) (2024)
- [CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations](https://mlsanity.com/paper/2601.19367/) (2026)
- [Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model](https://mlsanity.com/paper/2606.26373/) (2026)
- [Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming](https://mlsanity.com/paper/2302.08930/) (2023)
- [Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption](https://mlsanity.com/paper/2509.01253/) (2025)
- [FHE-Agent: Automating CKKS Configuration for Practical Encrypted Inference via an LLM-Guided Agentic Framework](https://mlsanity.com/paper/2511.18653/) (2025)
- [Decision-Aware Quadratic ReLU Replacement for HE-Friendly Inference](https://mlsanity.com/paper/2605.22237/) (2026)
- [Encrypted Neural Networks without Overflows](https://mlsanity.com/paper/2605.23096/) (2026)
- [Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption](https://mlsanity.com/paper/2606.05129/) (2026)
- [FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding](https://mlsanity.com/paper/2606.16359/) (2026)
- [SHARD: cell-keyed residual splitting for alignment-resistant private dense retrieval](https://mlsanity.com/paper/2606.27976/) (2026)
- [Fiona: Accelerating FHE Inference with Packing-Aware Ternary Weights](https://mlsanity.com/paper/2609.35352/) (2026)

## Abstract (arXiv)

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.