mlsanityData to 7 Oct 2026

nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data

arXiv 1810.10121 ↗First posted 23 Oct 2018cs.CRcited by 24

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Abstract (arXiv)

Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). However, building DL models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in DL, cryptography, and software engineering. DL frameworks and recent advances in graph compilers have greatly accelerated the training and deployment of DL models to various computing platforms. We introduce nGraph-HE, an extension of nGraph, Intel's DL graph compiler, which enables deployment of trained models with popular frameworks such as TensorFlow while simply treating HE as another hardware target. Our graph-compiler approach enables HE-aware optimizations-- implemented at compile-time, such as constant folding and HE-SIMD packing, and at run-time, such as special value plaintext bypass. Furthermore, nGraph-HE integrates with DL frameworks such as TensorFlow, enabling data scientists to benchmark DL models with minimal overhead.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 5; 2024: 4; 2025: 0; 2026: 1.

24 papers.

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  1. 2021Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Networkcited by 57
  2. 2022THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryptioncited by 55
  3. 2021TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryptioncited by 51
  4. 2021CaPC Learning: Confidential and Private Collaborative Learningcited by 33
  5. 2021SIRNN: A Math Library for Secure RNN Inferencecited by 28
  6. 2021Enabling Homomorphically Encrypted Inference for Large DNN Modelscited by 6
  7. 2022Private and Reliable Neural Network Inferencecited by 6
  8. 2023TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecturecited by 4
  9. 2023Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryptioncited by 4
  10. 2024Privacy-Preserving Diffusion Model Using Homomorphic Encryptioncited by 4
  11. 2021SoK: Privacy-preserving Deep Learning with Homomorphic Encryptioncited by 2
  12. 2022Efficient ML Models for Practical Secure Inferencecited by 2
12 more
  1. 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Servicecited by 2
  2. 2021FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Datacited by 1
  3. 2022Privacy-Preserving Distributed Machine Learning Made Fastercited by 1
  4. 2022Partially Oblivious Neural Network Inferencecited by 1
  5. 2023HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx modelscited by 1
  6. 2024Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvementcited by 1
  7. 2024A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanismcited by 1
  8. 2024Privacy-Preserving Hierarchical Model-Distributed Inferencecited by 1
  9. 2022ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizationscited by 0
  10. 2022SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHEcited by 0
  11. 2023Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programmingcited by 0
  12. 2026SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacycited by 0