Confidential Computing is an approach to data security that focuses on protecting data while it’s being processed or used by applications. It ensures that data remains encrypted even during computation, typically leveraging technologies like Trusted Execution Environments (TEEs) or secure enclaves. This approach offers protection for data in use, complementing traditional methods that secure data at rest and data in transit.

Large language models (LLMs) handle vast amounts of data and are often used in applications where they process sensitive or private information. Some areas that confidential computing can potentially address:

Secure Inference:

When users interact with a hosted LLM, they often send queries that might contain private or sensitive information. By running the inference process within a TEE, the data remains encrypted, ensuring that even the hosting provider or system administrators cannot access the raw queries.
This can be particularly useful for applications in healthcare, finance, or other sectors where data privacy is paramount.

Model Training:

While training data for public models is typically sanitized and doesn’t contain specific private information, there are scenarios where organizations might want to fine-tune these models on proprietary datasets. Using confidential computing, this fine-tuning can occur in a secure enclave, ensuring the training data remains confidential.
This allows organizations to leverage their data without exposing it, even in shared or public cloud environments.

Model Protection:

LLMs represent significant investments in terms of data collection, training time, and computational resources. Confidential computing can help protect the model from being copied or tampered with, especially when deployed in less trusted environments.

Collaborative Training:

Multiple organizations might want to collaboratively train a model without exposing their individual datasets to each other. Confidential computing can enable this by allowing data from different sources to be processed together without being directly accessible.

Regulatory Compliance:

For LLMs used in sectors with stringent data protection regulations (e.g., healthcare or finance), confidential computing can help in meeting compliance requirements related to data privacy and security.

Edge Deployments:

As LLMs find applications in edge devices, confidential computing can ensure that local data processing on these devices remains secure, especially in scenarios where the device might be physically accessible.

Reduced Attack Surface:

By ensuring data remains encrypted even during processing, the attack surface is reduced. Even if an attacker gains access to the system memory or intercepts data flow, they would only see encrypted data, making traditional data exfiltration or snooping attacks ineffective.

However, while confidential computing offers many advantages, it also introduces computational overhead and complexity. The performance impact might be a concern, especially for real-time applications of LLMs. As with any technology, it’s essential to weigh the benefits against the potential drawbacks and challenges.