5 citations · 5 across the 2 of their papers we have counts for
4 papers
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
Haochen Mei, Gaolei Li, Jun Wu +1
Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one ha…
ASFGNN: Automated Separated-Federated Graph Neural Network
Longfei Zheng, Jun Zhou, Chaochao Chen +3
Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relation…
Industrial Scale Privacy Preserving Deep Neural Network
Longfei Zheng, Chaochao Chen, Yingting Liu +6
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become…
Privacy Preserving PCA for Multiparty Modeling
Yingting Liu, Chaochao Chen, Longfei Zheng +4
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplis…