用少量数据让大模型精准分析用户隐私偏好，还更安全。
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
- 结合少样本学习与隐私计算，构建小数据下的隐私偏好模型。
- 在有限数据下，大模型的预测准确率显著高于传统方法。
- 融合差分隐私和联邦学习，有效降低用户数据泄露风险。
随着大语言模型（LLMs）的广泛应用，用户隐私保护成为重要研究课题。现有隐私偏好建模方法通常依赖大规模用户数据，在数据受限环境下难以有效分析。本研究探讨了在数据有限场景下，大语言模型如何分析用户隐私行为，并提出一种融合少样本学习与隐私计算的方法来建模用户隐私偏好。研究使用匿名化用户隐私设置数据、调查问卷回复及模拟数据，对比传统建模方法与基于大模型的方法性能。实验结果表明，即使数据有限，大语言模型也能显著提升隐私偏好建模的准确性。此外，引入差分隐私与联邦学习进一步降低了用户数据暴露风险。研究为大模型在隐私保护中的应用提供了新思路，也为隐私计算与用户行为分析的发展提供了理论支持。
原文摘要 · Abstract (English)
With the widespread application of large language models (LLMs), user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments. This study explores how LLMs can analyze user behavior related to privacy protection in scenarios with limited data and proposes a method that integrates Few-shot Learning and Privacy Computing to model user privacy preferences. The research utilizes anonymized user privacy settings data, survey responses, and simulated data, comparing the performance of traditional modeling approaches with LLM-based methods. Experimental results demonstrate that, even with limited data, LLMs significantly improve the accuracy of privacy preference modeling. Additionally, incorporating Differential Privacy and Federated Learning further reduces the risk of user data exposure. The findings provide new insights into the application of LLMs in privacy protection and offer theoretical support for advancing privacy computing and user behavior analysis.
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