MORSE-STF: A Privacy Preserving Computation System

• URL: http://arxiv.org/abs/2109.11726v1
• Date: Fri, 24 Sep 2021 03:42:46 GMT
• Title: MORSE-STF: A Privacy Preserving Computation System
• Authors: Qizhi Zhang, Yuan Zhao, Lichun Li, JiaoFu Zhang, Qichao Zhang, Yashun Zhou, Dong Yin, Sijun Tan, Shan Yin
• Abstract summary: We present Secure-TF, a privacy-preserving machine learning framework based on MPC. Our framework is able to support widely-used machine learning models such as logistic regression, fully-connected neural network, and convolutional neural network.
• Score: 12.875477499515158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy-preserving machine learning has become a popular area of research due to the increasing concern over data privacy. One way to achieve privacy-preserving machine learning is to use secure multi-party computation, where multiple distrusting parties can perform computations on data without revealing the data itself. We present Secure-TF, a privacy-preserving machine learning framework based on MPC. Our framework is able to support widely-used machine learning models such as logistic regression, fully-connected neural network, and convolutional neural network. We propose novel cryptographic protocols that has lower round complexity and less communication for computing sigmoid, ReLU, conv2D and there derivatives. All are central building blocks for modern machine learning models. With our more efficient protocols, our system is able to outperform previous state-of-the-art privacy-preserving machine learning framework in the WAN setting.

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