EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
DeepMind Blog·19d ago·5 min read
Advancing Private AI Compute with secure, server-side memory
TL;DR
DeepMind has developed a new Private AI Compute architecture that enables secure, server-side memory while maintaining strict on-device privacy standards. This innovation allows AI assistants to provide continuous support across devices without compromising user data security.
✦ Why It Matters
Consider implementing this secure memory architecture to enhance user privacy in your AI applications.
Key Takeaways
- Explore integrating server-side memory for enhanced AI capabilities.
- Ensure cryptographic keys remain on user devices for maximum privacy.
- Utilize secure enclaves for temporary data processing in the cloud.
- Adopt end-to-end encryption to protect user data during AI interactions.
How It Works
The Private AI Compute architecture functions by creating a secure digital vault in the cloud, where user data is encrypted. When an AI model needs to access this data, it connects through an end-to-end encrypted channel to a secure enclave.
This enclave temporarily decrypts the data for processing, then re-encrypts it after use, ensuring that user information remains private and secure throughout the interaction.