Does LLaMA have built-in mechanisms for ensuring data security during inference or training? Are there any recommended approaches for fine-tuning or querying the model on confidential datasets without risking data leakage? Can LLaMA be deployed in a completely offline environment for increased security?

[UQSam]

I am exploring ways to use LLaMA for topic modeling while ensuring data confidentiality. Could you please advise on the best practices or features that can facilitate secure and private processing of sensitive data? Specifically, I am interested in knowing:

Does LLaMA have built-in mechanisms for ensuring data security during inference or training?
Are there any recommended approaches for fine-tuning or querying the model on confidential datasets without risking data leakage?
Can LLaMA be deployed in a completely offline environment for increased security?

[MaartenGr]

Does LLaMA have built-in mechanisms for ensuring data security during inference or training?

Any LLM does not have built-in mechanisms for ensuring data security during inference or training. During inference, you can try to build in guardrails. During training, there is no easy way to prevent sensitive data from leaking other than anonymizing the data.

Are there any recommended approaches for fine-tuning or querying the model on confidential datasets without risking data leakage?

For querying the model, using a local model is already sufficient. There is no way for that data to end up somewhere else if you are using a local model.

Can LLaMA be deployed in a completely offline environment for increased security?

Definitely! Many llama-like models in BERTopic can be deployed offline without any risk of security issues (assuming you trust the model that you are using). You can find an overview here.

Make sure to always use a model saved with safetensors as that is much more safe than using pytorch files.