1 citations · 2 across the 6 of their papers we have counts for
6 papers
Data Agents Under Attack: Vulnerabilities in LLM-Driven Analytical Systems
Kuncan Wang, Ziting Wang, Peizhuo Lv +4
Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making them increasingly central to ente…
Acyclic Graph Pattern Counting under Local Differential Privacy
Yihua Hu, Kuncan Wang, Wei Dong
Graph pattern counting serves as a cornerstone of network analysis with extensive real-world applications. Its integration with local differential privacy (LDP) has gained growing…
Single-cell Curriculum Learning-based Deep Graph Embedding Clustering
Huifa Li, Jie Fu, Xinpeng Ling +3
The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contrib…
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
Xinpeng Ling, Jie Fu, Kuncan Wang +3
Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persiste…
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release
Jie Fu, Qingqing Ye, Haibo Hu +4
Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and member…
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Xinpeng Ling, Jie Fu, Kuncan Wang +2
Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw…