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A Note on Our Submission to Track 4 of iDASH 2019
Authors: Marcel Keller, Ke SunAbstract:
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. After seeing some winning solutions, we have lowered this figure to less than a second.
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