  The generative Artificial Intelligence (AI) tools based on Large Language
Models (LLMs) use billions of parameters to extensively analyse large datasets
and extract critical private information such as, context, specific details,
identifying information etc. This have raised serious threats to user privacy
and reluctance to use such tools. This article proposes the conceptual model
called PrivChatGPT, a privacy-preserving model for LLMs that consists of two
main components i.e., preserving user privacy during the data
curation/pre-processing together with preserving private context and the
private training process for large-scale data. To demonstrate its
applicability, we show how a private mechanism could be integrated into the
existing model for training LLMs to protect user privacy; specifically, we
employed differential privacy and private training using Reinforcement Learning
(RL). We measure the privacy loss and evaluate the measure of uncertainty or
randomness once differential privacy is applied. It further recursively
evaluates the level of privacy guarantees and the measure of uncertainty of
public database and resources, during each update when new information is added
for training purposes. To critically evaluate the use of differential privacy
for private LLMs, we hypothetically compared other mechanisms e..g, Blockchain,
private information retrieval, randomisation, for various performance measures
such as the model performance and accuracy, computational complexity, privacy
vs. utility etc. We conclude that differential privacy, randomisation, and
obfuscation can impact utility and performance of trained models, conversely,
the use of ToR, Blockchain, and PIR may introduce additional computational
complexity and high training latency. We believe that the proposed model could
be used as a benchmark for proposing privacy preserving LLMs for generative AI
tools.
