# Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation

arXiv: https://arxiv.org/abs/2108.07463 · HTML: https://mlsanity.com/paper/2108.07463/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 22 of 22)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [See through Gradients: Image Batch Recovery via GradInversion](https://mlsanity.com/paper/2104.07586/) (2021)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications](https://mlsanity.com/paper/1801.03239/) (2018)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [DeepSecure: Scalable Provably-Secure Deep Learning](https://mlsanity.com/paper/1705.08963/) (2017)
- [ASFGNN: Automated Separated-Federated Graph Neural Network](https://mlsanity.com/paper/2011.03248/) (2020)
- [BLAZE: Blazing Fast Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2005.09042/) (2020)
- [When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control](https://mlsanity.com/paper/2008.08753/) (2020)
- [Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning](https://mlsanity.com/paper/1912.02631/) (2019)
- [Large-Scale Secure XGB for Vertical Federated Learning](https://mlsanity.com/paper/2005.08479/) (2020)
- [SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2005.10296/) (2020)
- [ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction](https://mlsanity.com/paper/1912.02592/) (2019)
- [BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference](https://mlsanity.com/paper/1906.00639/) (2019)
- [Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation](https://mlsanity.com/paper/1908.07915/) (2019)
- [Privacy-preserving collaborative machine learning on genomic data using TensorFlow](https://mlsanity.com/paper/2002.04344/) (2020)
- [Industrial Scale Privacy Preserving Deep Neural Network](https://mlsanity.com/paper/2003.05198/) (2020)

## Built on by (most cited first, 5 of 5)

- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [Secure Transformer Inference Protocol](https://mlsanity.com/paper/2312.00025/) (2023)
- [CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference](https://mlsanity.com/paper/2412.10652/) (2024)
- [An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs](https://mlsanity.com/paper/2505.18332/) (2025)
- [On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference](https://mlsanity.com/paper/2605.04901/) (2026)

## Abstract (arXiv)

With the increasing demands for privacy protection, privacy-preserving machine learning has been drawing much attention in both academia and industry. However, most existing methods have their limitations in practical applications. On the one hand, although most cryptographic methods are provable secure, they bring heavy computation and communication. On the other hand, the security of many relatively efficient private methods (e.g., federated learning and split learning) is being questioned, since they are non-provable secure. Inspired by previous work on privacy-preserving machine learning, we build a privacy-preserving machine learning framework by combining random permutation and arithmetic secret sharing via our compute-after-permutation technique. Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods. Moreover, by adopting distance correlation as a metric for privacy leakage, we demonstrate that our method is more secure than previous non-provable secure methods. Overall, our proposal achieves a good balance between security and efficiency. Experimental results show that our method not only is up to 6x faster and reduces up to 85% network traffic compared with state-of-the-art cryptographic methods, but also leaks less privacy during the training process compared with non-provable secure methods.