2 citations · 2 across the 4 of their papers we have counts for
5 papers
OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
Jiangrui Yu, Ye Yu, Si Chen +5
Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the…
Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs
Huaming Ling, Ying Wang, Si Chen +1
We address two fundamental challenges in adapting general deep CNNs for FHE-based inference: approximating non-linear activations such as ReLU with low-degree polynomials while min…
SEEK: model extraction attack against hybrid secure inference protocols
Si Chen, Junfeng Fan
Security concerns about a machine learning model used in a prediction-as-a-service include the privacy of the model, the query and the result. Secure inference solutions based on h…
DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning
Zizhen Liu, Si Chen, Jing Ye +3
Secure aggregation is widely used in horizontal Federated Learning (FL), to prevent leakage of training data when model updates from data owners are aggregated. Secure aggregation…
SASH: Efficient Secure Aggregation Based on SHPRG For Federated Learning
Zizhen Liu, Si Chen, Jing Ye +3
To prevent private training data leakage in Fed?erated Learning systems, we propose a novel se?cure aggregation scheme based on seed homomor?phic pseudo-random generator (SHPRG), n…