The community explored to build private inference frameworks for transformer-based large
language models (LLMs) in a server-client setting, where the server holds the model …

The drastic increase in language models' parameters has led to a new trend of deploying
models in cloud servers, raising growing concerns about private inference for Transformer …

Large Language Models (LLMs) shows powerful capability in natural language
understanding by capturing hidden semantics in vector space. This process enriches the …

Recently, more and more pre-trained language models are released as a cloud service. It
allows users who lack computing resources to perform inference with a powerful model by …

Large language models (LLMs), such as ChatGPT, have simplified text generation tasks, yet
their inherent privacy risks are increasingly garnering attention. While differential privacy …

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 …

Large Language Models (LLMs) have emerged as dominant tools for various tasks,
particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns …

Protecting large language models from privacy leakage is becoming increasingly crucial
with their wide adoption in real-world products. Yet applying differential privacy (DP), a …

Recent papers have shown that large pre-trained language models (LLMs) such as BERT,
GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private …

Prompt tuning provides an efficient way for users to customize Large Language Models
(LLMs) with their private data in the emerging LLM service scenario. However, the sensitive …