High Performance Logistic Regression for Privacy-Preserving Genome Analysis
This work addresses privacy concerns in genome analysis for researchers and healthcare professionals, but it is incremental as it focuses on improving speed within an existing framework.
The paper tackled the problem of training logistic regression models on high-dimensional genome data while preserving privacy, achieving the fastest existing secure Multi-Party Computation implementation for this task.
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.