# Secure Data Sharing With Flow Model

arXiv: https://arxiv.org/abs/2009.11762 · HTML: https://mlsanity.com/paper/2009.11762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Computation-Efficient and Recognition-Friendly 3D Point Cloud Privacy Protection](https://mlsanity.com/paper/2503.15818/) (2025)

## Abstract (arXiv)

In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine learning training purposes, but the data are also encrypted so that they cannot be recovered by other parties. We present a rotation based method using flow model, and theoretically justified its security. We demonstrate the effectiveness of our method in different scenarios, including supervised secure model training, and unsupervised generative model training. Our code is available at https://github.com/ duchenzhuang/flowencrypt.