# Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52

arXiv: https://arxiv.org/abs/2103.16400 · HTML: https://mlsanity.com/paper/2103.16400/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2021.

## Built on by (most cited first, 8 of 8)

- [Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2202.13351/) (2022)
- [HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks](https://mlsanity.com/paper/2302.02407/) (2023)
- [MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference](https://mlsanity.com/paper/2209.13643/) (2022)
- [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)
- [ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications](https://mlsanity.com/paper/2306.11006/) (2023)
- [Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks](https://mlsanity.com/paper/2604.16834/) (2026)
- [ComputeFHE: A Privacy-Preserving General-Purpose Computation Library](https://mlsanity.com/paper/2606.24379/) (2026)
- [SHARD: cell-keyed residual splitting for alignment-resistant private dense retrieval](https://mlsanity.com/paper/2606.27976/) (2026)

## Abstract (arXiv)

Modern implementations of homomorphic encryption (HE) rely heavily on polynomial arithmetic over a finite field. This is particularly true of the CKKS, BFV, and BGV HE schemes. Two of the biggest performance bottlenecks in HE primitives and applications are polynomial modular multiplication and the forward and inverse number-theoretic transform (NTT). Here, we introduce Intel Homomorphic Encryption Acceleration Library (Intel HEXL), a C++ library which provides optimized implementations of polynomial arithmetic for Intel processors. Intel HEXL takes advantage of the recent Intel Advanced Vector Extensions 512 (Intel AVX512) instruction set to provide state-of-the-art implementations of the NTT and modular multiplication. On the forward and inverse NTT, Intel HEXL provides up to 7.2x and 6.7x speedup, respectively, over a native C++ implementation. Intel HEXL also provides up to 6.0x speedup on the element-wise vector-vector modular multiplication, and 1.7x speedup on the element-wise vector-scalar modular multiplication. Intel HEXL is available open-source at https://github.com/intel/hexl under the Apache 2.0 license and has been adopted by the Microsoft SEAL and PALISADE homomorphic encryption libraries.