User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910935594041344 |
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| author | Yang, Haowei Lu, Qingyi Wang, Yang Liu, Sibei Zheng, Jiayun Xiang, Ao |
| author_facet | Yang, Haowei Lu, Qingyi Wang, Yang Liu, Sibei Zheng, Jiayun Xiang, Ao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data Yang, Haowei Lu, Qingyi Wang, Yang Liu, Sibei Zheng, Jiayun Xiang, Ao Cryptography and Security Artificial Intelligence 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. |
| title | User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06305 |