4 citations · 6 across the 4 of their papers we have counts for
4 papers
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Mingxuan Fan, Yilun Jin, Liu Yang +2
Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features…
Secure Forward Aggregation for Vertical Federated Neural Networks
Shuowei Cai, Di Chai, Liu Yang +5
Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus…
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels
Qingqing Long, Yilun Jin, Yi Wu +1
Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Speci…
Ternary Hashing
Chang Liu, Lixin Fan, Kam Woh Ng +5
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the…