# Low Latency Privacy Preserving Inference

arXiv: https://arxiv.org/abs/1812.10659 · HTML: https://mlsanity.com/paper/1812.10659/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 30 of 57)

- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Nimbus: Secure and Efficient Two-Party Inference for Transformers](https://mlsanity.com/paper/2411.15707/) (2024)
- [AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE](https://mlsanity.com/paper/2310.08012/) (2023)
- [CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference](https://mlsanity.com/paper/2209.11904/) (2022)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (2022)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks](https://mlsanity.com/paper/2302.02407/) (2023)
- [Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations](https://mlsanity.com/paper/2206.05893/) (2022)
- [Private and Reliable Neural Network Inference](https://mlsanity.com/paper/2210.15614/) (2022)
- [Enabling Homomorphically Encrypted Inference for Large DNN Models](https://mlsanity.com/paper/2103.16139/) (2021)
- [Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)](https://mlsanity.com/paper/2201.12577/) (2022)
- [Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images](https://mlsanity.com/paper/2208.10919/) (2022)
- [Audit and Improve Robustness of Private Neural Networks on Encrypted Data](https://mlsanity.com/paper/2209.09996/) (2022)
- [TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture](https://mlsanity.com/paper/2302.01584/) (2023)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [Leveraging ASIC AI Chips for Homomorphic Encryption](https://mlsanity.com/paper/2501.07047/) (2025)
- [A Training Framework for Optimal and Stable Training of Polynomial Neural Networks](https://mlsanity.com/paper/2505.11589/) (2025)
- [Application of Homomorphic Encryption in Medical Imaging](https://mlsanity.com/paper/2110.07768/) (2021)
- [Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service](https://mlsanity.com/paper/2310.06840/) (2023)
- [CryptoFace: End-to-End Encrypted Face Recognition](https://mlsanity.com/paper/2509.00332/) (2025)
- [Efficient CNN Building Blocks for Encrypted Data](https://mlsanity.com/paper/2102.00319/) (2021)
- [FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data](https://mlsanity.com/paper/2102.03494/) (2021)
- [Efficient Encrypted Inference on Ensembles of Decision Trees](https://mlsanity.com/paper/2103.03411/) (2021)
- [Enabling Inference Privacy with Adaptive Noise Injection](https://mlsanity.com/paper/2104.02261/) (2021)
- [On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence](https://mlsanity.com/paper/2302.05323/) (2023)
- [HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models](https://mlsanity.com/paper/2302.08260/) (2023)
- [Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference](https://mlsanity.com/paper/2402.01296/) (2024)

## Abstract (arXiv)

When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than 10× improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of ~0.16 seconds. We demonstrate the efficacy of our methods on several computer vision tasks.