The rapid adoption of online chatbots represents a significant advancement in
artificial intelligence. However, this convenience brings considerable privacy
concerns, as prompts can inadvertently contain sensitive information exposed to
large language models (LLMs). Limited by high computational costs, reduced task
usability, and excessive system modifications, previous works based on local
deployment, embedding perturbation, and homomorphic encryption are inapplicable
to online prompt-based LLM applications.
To address these issues, this paper introduces Prompt Privacy Sanitizer
(i.e., ProSan), an end-to-end prompt privacy protection framework that can
produce anonymized prompts with contextual privacy removed while maintaining
task usability and human readability. It can also be seamlessly integrated into
the online LLM service pipeline. To achieve high usability and dynamic
anonymity, ProSan flexibly adjusts its protection targets and strength based on
the importance of the words and the privacy leakage risk of the prompts.
Additionally, ProSan is capable of adapting to diverse computational resource
conditions, ensuring privacy protection even for mobile devices with limited
computing power. Our experiments demonstrate that ProSan effectively removes
private information across various tasks, including question answering, text
summarization, and code generation, with minimal reduction in task performance.

为解决在线语言模型应用中的隐私问题，本文介绍了一种名为 ProSan 的端到端提示隐私保护框架，它可以产生去除上下文隐私的匿名提示，同时保持任务可用性和人类可读性。ProSan 还能够灵活调整其保护目标和强度，适应不同的计算资源条件，确保即使在计算能力有限的移动设备上也能提供隐私保护。实验证明，ProSan 在各种任务中，包括问题回答、文本摘要和代码生成，有效去除私人信息，并仅对任务性能产生最小的影响。

火焰窃贼也是守护者：平衡提示中的可用性与隐私

The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in  Prompts

Recent research has made impressive progress in single-turn dialogue
modelling. In the multi-turn setting, however, current models are still far
from satisfactory. One major challenge is the frequently occurred coreference
and information omission in our daily conversation, making it hard for machines
to understand the real intention. In this paper, we propose rewriting the human
utterance as a pre-process to help multi-turn dialgoue modelling. Each
utterance is first rewritten to recover all coreferred and omitted information.
The next processing steps are then performed based on the rewritten utterance.
To properly train the utterance rewriter, we collect a new dataset with human
annotations and introduce a Transformer-based utterance rewriting architecture
using the pointer network. We show the proposed architecture achieves
remarkably good performance on the utterance rewriting task. The trained
utterance rewriter can be easily integrated into online chatbots and brings
general improvement over different domains.

该研究提出了一种将人类话语重写作为预处理方法，以帮助多轮对话建模。使用指针网络提出的基于 Transformer 的话语重写架构训练的话语重写器可以轻松地集成到在线聊天机器人中，并在不同领域带来一般性能的提升。