5 papers
FESC: Remodeling Long-Context Private Inference with Encrypted State-Space Models
Yufan Zhu, Chao Jin, Khin Mi Mi Aung +1
Processing long, sensitive documents with machine-learning models requires efficient, privacy-preserving long-context inference. Prior private inference systems optimize or distrib…
EncFormer: Secure and Efficient Transformer Inference over Encrypted Data
Yufan Zhu, Chao Jin, Khin Mi Mi Aung +1
Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (…
Predicting Cascading Failures with a Hyperparametric Diffusion Model
Bin Xiang, Bogdan Cautis, Xiaokui Xiao +3
In this paper, we study cascading failures in power grids through the lens of information diffusion models. Similar to the spread of rumors or influence in an online social network…
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang, Wencai Cao, Hoang Ta +2
Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are propose…
Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation
Keke Huang, Ruize Gao, Bogdan Cautis +1
The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are acces…