27 citations · 49 across the 13 of their papers we have counts for
4 papers · 1 filter
User Consented Federated Recommender System Against Personalized Attribute Inference Attack
Qi Hu, Yangqiu Song
Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representatio…
Privacy-Preserved Neural Graph Databases
Qi Hu, Haoran Li, Jiaxin Bai +2
In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval au…
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models
Haoran Li, Dadi Guo, Donghao Li +7
The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art perfor…
Privacy in Large Language Models: Attacks, Defenses and Future Directions
Haoran Li, Yulin Chen, Jinglong Luo +9
The advancement of large language models (LLMs) has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipe…