EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 22·5 min read
I Built a Private Genomics Study with Stoffel MPC
TL;DR
A proof of concept using Stoffel MPC demonstrated that genomic studies can yield valuable insights without collecting participants' DNA, ensuring privacy. One hundred simulated participants successfully computed aggregate allele counts while maintaining data confidentiality.
✦ Why It Matters
Consider using Stoffel MPC for privacy-preserving applications in your next data project.
Key Takeaways
- Explore using Stoffel MPC for privacy-preserving applications.
- Test the platform with simulated data before real-world deployment.
- Ensure independent operation of MPC parties for enhanced security.
- Consider staggering client connections to avoid network issues.
How It Works
Stoffel MPC allows multiple parties to compute on private data without any single party seeing the complete dataset. Each user generates shares of their data locally and sends them to different MPC parties, which collaboratively compute aggregate results.
This method ensures that individual data remains private while still enabling useful analysis.