PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models

• URL: http://arxiv.org/abs/2502.13564v1
• Date: Wed, 19 Feb 2025 09:17:07 GMT
• Title: PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models
• Authors: Guangwei Li, Yuansen Zhang, Yinggui Wang, Shoumeng Yan, Lei Wang, Tao Wei,
• Abstract summary: We propose a privacy preservation pipeline for protecting privacy and sensitive information during interactions between users and large language models. We construct SensitiveQA, the first privacy open-ended question-answering dataset. Our proposed solution employs a multi-stage strategy aimed at preemptively securing user information while simultaneously preserving the response quality of cloud-based LLMs.
• Score: 10.050972891318324
• License:
• Abstract: The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is becoming increasingly prevalent. However, transmitting user data to cloud-based LLMs presents significant risks of data breaches and unauthorized access to personal identification information. In this paper, we propose a privacy preservation pipeline for protecting privacy and sensitive information during interactions between users and LLMs in practical LLM usage scenarios. We construct SensitiveQA, the first privacy open-ended question-answering dataset. It comprises 57k interactions in Chinese and English, encompassing a diverse range of user-sensitive information within the conversations. Our proposed solution employs a multi-stage strategy aimed at preemptively securing user information while simultaneously preserving the response quality of cloud-based LLMs. Experimental validation underscores our method's efficacy in balancing privacy protection with maintaining robust interaction quality. The code and dataset are available at https://github.com/ligw1998/PRIV-QA.

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