# User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data

arXiv: https://arxiv.org/abs/2505.06305 · HTML: https://mlsanity.com/paper/2505.06305/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 26 of 26)

- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [ComposerX: Multi-Agent Symbolic Music Composition with LLMs](https://mlsanity.com/paper/2404.18081/) (2024)
- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion](https://mlsanity.com/paper/2501.04606/) (2025)
- [RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts](https://mlsanity.com/paper/2405.13179/) (2024)
- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics](https://mlsanity.com/paper/2404.16296/) (2024)
- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning](https://mlsanity.com/paper/2408.03084/) (2024)
- [Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies](https://mlsanity.com/paper/2503.00724/) (2025)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models](https://mlsanity.com/paper/2501.07033/) (2025)
- [Machine Learning-Based Research on the Adaptability of Adolescents to Online Education](https://mlsanity.com/paper/2408.16849/) (2024)
- [DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment](https://mlsanity.com/paper/2409.03930/) (2024)
- [Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm](https://mlsanity.com/paper/2408.12672/) (2024)
- [Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data](https://mlsanity.com/paper/2410.14963/) (2024)
- [Real-Time Prediction for Athletes' Psychological States Using BERT-XGBoost: Enhancing Human-Computer Interaction](https://mlsanity.com/paper/2412.05816/) (2024)
- [Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions](https://mlsanity.com/paper/2502.00339/) (2025)
- [Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning](https://mlsanity.com/paper/2411.15721/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [II-NVM: Enhancing Map Accuracy and Consistency with Normal Vector-Assisted Mapping](https://mlsanity.com/paper/2504.08204/) (2025)
- [A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field](https://mlsanity.com/paper/2501.04996/) (2025)
- [Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review](https://mlsanity.com/paper/2502.00201/) (2025)

## Built on by (most cited first, 8 of 8)

- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2506.17551/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders](https://mlsanity.com/paper/2508.05289/) (2025)
- [Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain](https://mlsanity.com/paper/2507.07854/) (2025)
- [LLM-Augmented Symptom Analysis for Cardiovascular Disease Risk Prediction: A Clinical NLP](https://mlsanity.com/paper/2507.11052/) (2025)
- [Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms](https://mlsanity.com/paper/2509.04751/) (2025)

## Abstract (arXiv)

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.