Open Source versus Proprietary LLM Privacy

Understanding LLM Privacy

Large Language Models (LLMs) have become integral in various applications, making privacy a paramount consideration. Whether open source or proprietary, these models handle significant amounts of user data, raising questions about data security and privacy.

Open Source LLMs and Privacy

Open source LLMs offer transparency, enabling users to understand how the model processes data. This openness can lead to a better understanding of data handling practices, potentially enhancing trust. However, the lack of a centralised authority can lead to varied interpretations of privacy best practices.

Proprietary LLMs and Privacy

Proprietary LLMs often come with robust privacy policies governed by the organisations that develop them. These policies offer consistency but at the price of transparency, as the inner workings of the models remain hidden. Users must trust the company's adherence to privacy standards, which can be risky if such standards are not publicly audited.

Challenges in Maintaining Privacy

Both open source and proprietary LLMs face challenges in maintaining privacy, such as data breach risks, unauthorised access, and model inversion attacks. Both types need to ensure robust encryption and access controls to protect user data.