# ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications

arXiv: https://arxiv.org/abs/2306.11006 · HTML: https://mlsanity.com/paper/2306.11006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference](https://mlsanity.com/paper/1811.09953/) (2018)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [Going Beyond Classification Accuracy Metrics in Model Compression](https://mlsanity.com/paper/2012.01604/) (2020)
- [Secure Human Action Recognition by Encrypted Neural Network Inference](https://mlsanity.com/paper/2104.09164/) (2021)
- [Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52](https://mlsanity.com/paper/2103.16400/) (2021)
- [MATCHA: A Fast and Energy-Efficient Accelerator for Fully Homomorphic Encryption over the Torus](https://mlsanity.com/paper/2202.08814/) (2022)
- [A General Purpose Transpiler for Fully Homomorphic Encryption](https://mlsanity.com/paper/2106.07893/) (2021)
- [CPU and GPU Accelerated Fully Homomorphic Encryption](https://mlsanity.com/paper/2005.01945/) (2020)

## Abstract (arXiv)

Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmetic computations directly on encrypted data without decrypting it. Thus, even when processed by untrusted systems, confidential data is never exposed. In this work, we develop new techniques for accelerated encrypted execution and demonstrate the significant performance advantages of our approach. Our current focus is the Fully Homomorphic Encryption over the Torus (CGGI) scheme, which is a current state-of-the-art method for evaluating arbitrary functions in the encrypted domain. CGGI represents a computation as a graph of homomorphic logic gates and each individual bit of the plaintext is transformed into a polynomial in the encrypted domain. Arithmetic on such data becomes very expensive: operations on bits become operations on entire polynomials. Therefore, evaluating even relatively simple nonlinear functions, such as a sigmoid, can take thousands of seconds on a single CPU thread. Using our novel framework for end-to-end accelerated encrypted execution called ArctyrEX, developers with no knowledge of complex FHE libraries can simply describe their computation as a C program that is evaluated over 40× faster on an NVIDIA DGX A100 and 6× faster with a single A100 relative to a 256-threaded CPU baseline.