Secure Data Sharing With Flow Model

• URL: http://arxiv.org/abs/2009.11762v1
• Date: Thu, 24 Sep 2020 15:40:14 GMT
• Title: Secure Data Sharing With Flow Model
• Authors: Chenwei Wu, Chenzhuang Du, Yang Yuan
• Abstract summary: 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 so that they cannot be recovered by other parties. We present a rotation based method using flow model, and theoretically justified its security.
• Score: 13.773737296144366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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