research
∙
08/20/2023
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highl...
share
research
∙
06/27/2023
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Two-party computation (2PC) is promising to enable privacy-preserving de...
research
∙
02/05/2023
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of priv...
research
∙
09/20/2022
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emer...
research
∙
03/09/2022
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy
In this paper, we consider secure outsourced growing databases that supp...
research
∙
01/28/2022
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient ...
research
∙
03/29/2021
DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy
In this paper, we have introduced a new type of leakage associated with ...
research
∙
09/14/2020
SAPAG: A Self-Adaptive Privacy Attack From Gradients
Distributed learning such as federated learning or collaborative learnin...
research
∙
09/03/2020
ESMFL: Efficient and Secure Models for Federated Learning
Deep Neural Networks are widely applied to various domains. The successf...
research
∙
08/28/2020
MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks
Deep learning or deep neural networks (DNNs) have nowadays enabled high ...
research
∙
02/20/2019
