# Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

arXiv: https://arxiv.org/abs/1811.00778 · HTML: https://mlsanity.com/paper/1811.00778/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [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)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges](https://mlsanity.com/paper/2204.05136/) (2022)
- [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)
- [Ten Quick Tips for Deep Learning in Biology](https://mlsanity.com/paper/2105.14372/) (2021)
- [Encrypted Linear Contextual Bandit](https://mlsanity.com/paper/2103.09927/) (2021)
- [Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption](https://mlsanity.com/paper/2211.06675/) (2022)

## Abstract (arXiv)

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead.