27 citations · 27 across the 1 of their papers we have counts for
4 papers
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Privacy and Integrity Preserving Training Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting acceler…
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Protecting the privacy of input data is of growing importance as machine learning methods reach new application domains. In this paper, we provide a unified training and inference…
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments
Krishna Giri Narra, Zhifeng Lin, Yongqin Wang +2
This work presents Origami, which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blindin…