I Built a Private Genomics Study with Stoffel MPC

thisisauserid

MPC for Multi-Party Computation, not for Model Predictive Control.

usernametaken29

In genetics the end user is never the consumer. You have to collect blood, prepare it, sequence it. These are all highly specialised steps in which something will and can go wrong, and that’s why typically a hospital carries them out. That’s before even getting the device to do sequencing. The cheapest nanopore comes you at a couple ten thousand euros. Given the premise your work strikes me as oddly theoretical. Who is that mystery home DNA lab that needs decentralised private compute? Also, and funnily enough, for much of the genomic pipelines to run (eg a paternity test or a cancer test), a home computer is sufficient. Again, of course, you would need proper medical training to read, interpret and judge the results, which is why a hospital does it… so what problem exactly are you solving?

optionalsquid

> Most genomic studies begin by asking participants to upload one of the most identifying and irrevocable pieces of data they own, their DNA. I'm curious what the author means by "asking participants to upload ... their DNA", because I am not aware of any study that asked participants to do that.

doctorpangloss

It's a very interesting project. What are the big picture goals? I am asking from the POV of having spoken to many people in the genomics data ecosystem, such as pharmaceutical "chief commercialization officers," not just engineers.

badcryptobitch

Founder of stoffelmpc.com here. Thank you vishakh82 for sharing. It's great seeing folks apply PETs to key problems, no matter how theoretical they might seem now as evidenced by the comments. PoCs like this are steps in the right direction to make such work and ideas practical. If any of you are looking to try out Stoffel, you can check out our docs at docs.stoffelmpc.com or go to our github repo: https://github.com/Stoffel-Labs/stoffel

mfld

Very interesting project! I can imagine usefulness even beyond the intended use case, namely in joining the information from multiple large genomics studies. The sharing and access of such data becomes increasingly difficult, and by this approach one could increase the effective sample size while keeping the data private.