A Note on Our Submission to Track 4 of iDASH 2019
This work addresses secure and efficient machine learning training for sensitive biological data, but it is incremental as it describes a competition submission using existing methods.
The authors tackled the problem of training a machine learning model for cancer research datasets using multi-party computation (MPC) in a competition setting, achieving training times of less than one minute on three AWS instances with one semi-honest corruption and under ten seconds at slightly lower accuracy.
iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology. In 2019, one track asked for multi-party computation implementations of training of a machine learning model suitable for two datasets from cancer research. In this note, we describe our solution submitted to the competition. We found that the training can be run on three AWS c5.9xlarge instances in less then one minute using MPC tolerating one semi-honest corruption, and less than ten seconds at a slightly lower accuracy.