mlsanityData to 7 Oct 2026

ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications

arXiv 2306.11006 ↗First posted 19 Jun 2023cs.CR cs.AI cs.DCcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 50 references that are in the map. First posted on arXiv 19 Jun 2023.

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References · 8

  1. 2018Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inferencecited by 45
  2. 2018Secure Face Matching Using Fully Homomorphic Encryptioncited by 29
  3. 2020Going Beyond Classification Accuracy Metrics in Model Compressioncited by 15
  4. 2021Secure Human Action Recognition by Encrypted Neural Network Inferencecited by 10
  5. 2021Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52cited by 8
  6. 2022MATCHA: A Fast and Energy-Efficient Accelerator for Fully Homomorphic Encryption over the Toruscited by 3
  7. 2021A General Purpose Transpiler for Fully Homomorphic Encryptioncited by 2
  8. 2020CPU and GPU Accelerated Fully Homomorphic Encryptioncited by 1

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