Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Mar 2021.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 4.
8 papers.
- 2022Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryptioncited by 11
- 2023HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networkscited by 9
- 2022MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inferencecited by 4
- 2026Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat modelcited by 1
- 2023ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applicationscited by 0
- 2026Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networkscited by 0
- 2026ComputeFHE: A Privacy-Preserving General-Purpose Computation Librarycited by 0
- 2026SHARD: cell-keyed residual splitting for alignment-resistant private dense retrievalcited by 0
- 2026SHARD: cell-keyed residual splitting for alignment-resistant private dense retrieval26 Jun 2026
- 2026Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model24 Jun 2026
- 2026ComputeFHE: A Privacy-Preserving General-Purpose Computation Library23 Jun 2026
- 2026Towards Deep Encrypted Training: Low-Latency, Memory-Efficient, and High-Throughput Inference for Privacy-Preserving Neural Networks18 Apr 2026
- 2023ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications19 Jun 2023
- 2023HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks5 Feb 2023
- 2022MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference27 Sep 2022
- 2022Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption27 Feb 2022