The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts
Zhili Shen, Zihang Xi, Ying He, Wei Tong, Jingyu Hua, Sheng Zhong

TL;DR
This paper presents ProSan, an end-to-end framework that anonymizes prompts in online LLM applications, balancing privacy protection with task usability and adaptability across different computational resources.
Contribution
ProSan is a novel prompt sanitization framework that dynamically adjusts privacy levels, maintains readability, and integrates seamlessly into online LLM services.
Findings
Effectively removes private information in various tasks
Maintains high task performance with minimal accuracy loss
Adapts to diverse computational resource conditions
Abstract
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…
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Taxonomy
TopicsPrivacy, Security, and Data Protection
Methodstravel james
