activity
20232025
most citedUniversally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2025

SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses

Hanbin Hong, Shuang Wu, Shuya Feng +6

Large Language Models (LLMs) are increasingly used as interfaces to information, code, and real-world services, making prompt-level security failures a practical concern. Although…

cs.IR2025

RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models

Can Jin, Hongwu Peng, Anxiang Zhang +8

In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuan…

cs.CV2024

GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation

Hanbin Hong, Shenao Yan, Shuya Feng +2

Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model tr…

cs.LG20241 cited

Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence

Shuya Feng, Meisam Mohammady, Hanbin Hong +4

Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tra…

cs.CR2023

DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming

Shuya Feng, Meisam Mohammady, Han Wang +3

Streaming data, crucial for applications like crowdsourcing analytics, behavior studies, and real-time monitoring, faces significant privacy risks due to the large and diverse data…