Training Large Language Models (LLMs) on public data can pose specific privacy risks due to the potential for unintended exposure of sensitive information. When LLMs are trained on public data, they have the capacity to learn and replicate patterns, including personal or sensitive information, present in the training data. This can lead to privacy concerns, as the models may inadvertently memorize and reproduce private details, such as medical history, financial records, or personally identifiable information. Additionally, there is a risk of re-identification, where seemingly anonymized data in the training set can be used to identify individuals when combined with other available information. This can compromise the privacy of individuals who contributed to the public training data, as well as those whose information is indirectly inferred by the model.
To illustrate, training LLMs on public data is akin to a library that inadvertently memorizes and repeats the personal conversations and private details of its visitors. Just as a library should respect the privacy of its patrons and safeguard their personal information, LLMs trained on public data must be carefully managed to prevent the unintentional disclosure of sensitive details. Without proper safeguards, the models can inadvertently reveal private information, much like a library that inadvertently broadcasts the private conversations of its visitors to the public, compromising their privacy.
Please note that the provided answer is a brief overview; for a comprehensive exploration of privacy, privacy-enhancing technologies, and privacy engineering, as well as the innovative contributions from our students at Carnegie Mellon’s Privacy Engineering program, we highly encourage you to delve into our in-depth articles available through our homepage at https://privacy-engineering-cmu.github.io/.
Author: My name is Aman Priyanshu, you can check out my website for more details or check out my other socials: LinkedIn and Twitter