EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 4·5 min read
Updates on HEIR, the Homomorphic Encryption Compiler Project
TL;DR
HEIR is a homomorphic encryption compiler that enables secure computation on encrypted data, ensuring no information about the original data is exposed. It can compile pre-trained machine learning models for private inference, exemplified by a credit card fraud detection model.
✦ Why It Matters
Explore HEIR to implement secure machine learning models that protect user data privacy today.
Key Takeaways
- Experiment with HEIR to compile ML models for encrypted data processing.
- Consider the trade-offs between performance and privacy in your applications.
- Stay updated on HEIR's developments for potential integration into your projects.
- Evaluate the feasibility of using HEIR based on your model's complexity and user privacy needs.
How It Works
HEIR compiles machine learning models into a format that can process encrypted data, ensuring that no information about the original inputs or outputs is revealed during computation. It uses homomorphic encryption, which allows operations to be performed on ciphertexts, producing encrypted results that can be decrypted later.