# Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

arXiv: https://arxiv.org/abs/1811.09953 · HTML: https://mlsanity.com/paper/1811.09953/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2018.

## Built on by (most cited first, 30 of 45)

- [Offsite-Tuning: Transfer Learning without Full Model](https://mlsanity.com/paper/2302.04870/) (2023)
- [MPCFormer: fast, performant and private Transformer inference with MPC](https://mlsanity.com/paper/2211.01452/) (2022)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration](https://mlsanity.com/paper/2204.01701/) (2022)
- [Circa: Stochastic ReLUs for Private Deep Learning](https://mlsanity.com/paper/2106.08475/) (2021)
- [AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE](https://mlsanity.com/paper/2310.08012/) (2023)
- [Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2107.12342/) (2021)
- [FedVLN: Privacy-preserving Federated Vision-and-Language Navigation](https://mlsanity.com/paper/2203.14936/) (2022)
- [Ditto: Quantization-aware Secure Inference of Transformers upon MPC](https://mlsanity.com/paper/2405.05525/) (2024)
- [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)
- [Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference](https://mlsanity.com/paper/2405.17485/) (2024)
- [Enabling Homomorphically Encrypted Inference for Large DNN Models](https://mlsanity.com/paper/2103.16139/) (2021)
- [MPC-Minimized Secure LLM Inference](https://mlsanity.com/paper/2408.03561/) (2024)
- [Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)](https://mlsanity.com/paper/2201.12577/) (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)
- [Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption](https://mlsanity.com/paper/2310.10349/) (2023)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [Efficient Pruning for Machine Learning Under Homomorphic Encryption](https://mlsanity.com/paper/2207.03384/) (2022)
- [Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits](https://mlsanity.com/paper/2302.06361/) (2023)
- [Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service](https://mlsanity.com/paper/2310.06840/) (2023)
- [From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks](https://mlsanity.com/paper/2405.06038/) (2024)
- [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)
- [Partially Oblivious Neural Network Inference](https://mlsanity.com/paper/2210.15189/) (2022)
- [Privacy-Preserving 3-Layer Neural Network Training](https://mlsanity.com/paper/2308.09531/) (2023)
- [SelectFormer: Private and Practical Data Selection for Transformers](https://mlsanity.com/paper/2310.02373/) (2023)
- [Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference](https://mlsanity.com/paper/2402.01296/) (2024)

## Abstract (arXiv)

Homomorphic encryption enables arbitrary computation over data while it remains encrypted. This privacy-preserving feature is attractive for machine learning, but requires significant computational time due to the large overhead of the encryption scheme. We present Faster CryptoNets, a method for efficient encrypted inference using neural networks. We develop a pruning and quantization approach that leverages sparse representations in the underlying cryptosystem to accelerate inference. We derive an optimal approximation for popular activation functions that achieves maximally-sparse encodings and minimizes approximation error. We also show how privacy-safe training techniques can be used to reduce the overhead of encrypted inference for real-world datasets by leveraging transfer learning and differential privacy. Our experiments show that our method maintains competitive accuracy and achieves a significant speedup over previous methods. This work increases the viability of deep learning systems that use homomorphic encryption to protect user privacy.