Log In Sign Up

MORSE-STF: A Privacy Preserving Computation System

by   Qizhi Zhang, et al.

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.


PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers

Safeguarding privacy in machine learning is highly desirable, especially...

MPC Protocol for G-module and its Application in Secure Compare and ReLU

Secure multi-party computation (MPC) is a subfield of cryptography. Its ...

Efficient Deep Learning on Multi-Source Private Data

Machine learning models benefit from large and diverse datasets. Using s...

ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning

Cooperative learning, that enables two or more data owners to jointly tr...

PINFER: Privacy-Preserving Inference for Machine Learning

The foreseen growing role of outsourced machine learning services is rai...

CECILIA: Comprehensive Secure Machine Learning Framework

Since machine learning algorithms have proven their success in data mini...

pMPL: A Robust Multi-Party Learning Framework with a Privileged Party

In order to perform machine learning among multiple parties while protec...