354 citations · 835 across the 22 of their papers we have counts for
6 papers · 1 filter
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning 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 or infer with private data while exploitin…
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
Tiantian Feng, Hanieh Hashemi, Rajat Hebbar +2
Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech da…
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables
Rachit Rajat, Yongqin Wang, Murali Annavaram
Data confidentiality is becoming a significant concern, especially in the cloud computing era. Memory access patterns have been demonstrated to leak critical information such as se…
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…