21 citations · 67 across the 13 of their papers we have counts for
5 papers · 1 filter
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Jun Zhou, Longfei Zheng, Chaochao Chen +6
Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has b…
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…
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen, Jun Zhou, Longfei Zheng +7
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between…
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…