REVIEW 7 cited by
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 7 Pith papers
-
Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
A standard attention-fusion plus Transformer sequence model is applied to short-video recommendation, with claimed gains over weak baselines and no reproducible artifacts.
-
Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs
A meta-learned prompt-tuning method for cold-start LLM recommendations reports better Hit@10 and nDCG@10 on MovieLens-1M, but with no code, no error bars, and no shown results for Amazon or Recbole.
-
Research on Low-Latency Inference and Training Efficiency Optimization for Graph Neural Network and Large Language Model-Based Recommendation Systems
A hybrid GNN-LLM recommender with FPGA, DeepSpeed, and LoRA reportedly reaches NDCG@10 of 0.75 at 40-60ms latency while cutting training time by 66%, but the supporting artifacts are absent.
-
LLM-Augmented Symptom Analysis for Cardiovascular Disease Risk Prediction: A Clinical NLP
A small synthetic study reports that Bio_ClinicalBERT embeddings with Random Forest classify CVD risk in about 20 hand-written symptom texts, but the claims of MIMIC-III and CARDIO-NLP evaluation are unsupported.
-
Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
A hybrid model-plus-data parallel scheme is reported to boost training throughput and GPU utilization for LLM-based recommenders, but the supporting experiments are not reproducible from the paper.
-
Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks
A hybrid LLM-plus-GNN recommender is claimed to beat collaborative filtering, LLM-only, and GNN-only baselines on financial product ranking, with NDCG@10 of 0.372.
-
LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion
An LLM copywriting pipeline combining fine-tuning, vector search, and weighted reranking reportedly lifts CTR by 12.5% and CVR by 8.3%, but the evidence is unverifiable and internally inconsistent.
Discussion (0). Continue with ORCID to comment.