I Built a Private Genomics Study with Stoffel MPC
A developer has created a proof-of-concept genomics study using Stoffel's multi-party computation (MPC) platform to analyze DNA data without centralizing sensitive information. This approach allows researchers to compute aggregate statistics while keeping individual genetic records private and decentralized.
Why it matters
This technology addresses critical privacy concerns in medical research, potentially enabling large-scale genomic studies without the risk of massive data breaches.
Most genomic studies begin by asking participants to upload one of the most identifying and irrevocable pieces of data they own, their DNA. I continue to investigate whether multi-party computation can offer a different model, one in which a study can produce useful results without anyone collecting the participants’ genomes in the first place.
That question has now led me to build a proof of concept with Stoffel MPC. One hundred simulated participants successfully computed aggregate allele counts without any party seeing the complete dataset. This post explains what I built, how it performed and what I learned.
Stoffel recently launched version 0.1.0 of its multi-party computation (MPC) platform with the ambitious goal of making privacy-preserving applications accessible to ordinary developers and not just to teams of cryptography boffins.
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