Secure-RAG Use Case
Retrieval-Augmented Generation (RAG) in healthcare combines the power of large language models (LLMs) with external knowledge sources to deliver precise and contextually relevant insights.
Interactive UI-Demo
An interactive demo is available to showcase how Secure-RAG functions, allowing users to explore its features firsthand. The demo includes a client-side interface where users can input prompts and see RAG in action, with a visualization of seamless encryption processes running in the background. It highlights that privacy-preserving features do not compromise functionality, enabling users to generate secure and insightful responses with ease. Simply click on the prompt to randomly generate a question, submit it to start the demo, and return to resubmit as needed.
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Private Healthcare
By retrieving information from secure, up-to-date medical databases, RAG can provide answers to complex healthcare queries, assist in diagnostics, recommend treatments, and enhance decision-making for both patients and clinicians.
Privacy-Preserving
When integrated with encryption technologies, RAG ensures that sensitive patient data and proprietary medical knowledge remain confidential throughout the process, making it a transformative solution for secure, personalized healthcare delivery.
Key Features
Privacy
Ensures complete confidentiality of sensitive information, whether it’s the data or model weights.
Neither the model nor the data is exposed in plaintext during the entire inference process.
Performance
The architecture will enable efficient encrypted computations with minimal latency.
Minimizes communication bottlenecks, ensuring smooth workflows regardless of whether the data and model are owned by one or two entities.
Resource Management
Encrypted data reduce vulnerability risks, as no unencrypted assets are stored.
The AUX Server handles encrypted assets securely without requiring decryption.
The size of encrypted data are nearly the same as clear ones
Architecure
Secure RAG employs a robust architecture designed for privacy-preserving computations, where all data entities are encrypted before being sent to the system, ensuring seamless compatibility with secure computation methods. In this workflow, users encrypt their data on the client side before transmitting it to the server. The server processes the encrypted data securely and returns the results in encrypted form, requiring only decryption by the user upon receipt, ensuring end-to-end data protection without compromising usability.
The user experience is streamlined encryption and decryption occur seamlessly, with no additional manual effort. This means you can focus on using the system without needing any technical knowledge about how the security works.
Inside Secure-RAG
This demo video illustrates how Secure-RAG operates within the encryption workflow, highlighting the differences from both the client’s and server’s perspectives. It also explains the behind-the-scenes efforts to ensure the privacy of both user queries and the accessed resources.
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