133 citations · 251 across the 26 of their papers we have counts for
5 papers · 1 filter
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning
Weizhe Hua, Muhammad Umar, Zhiru Zhang +1
This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows tha…
Strong Asymptotic Composition Theorems for Mutual Information Measures
Benjamin Wu, Aaron B. Wagner, Ibrahim Issa +1
We characterize the growth of the Sibson and Arimoto mutual informations and -maximal leakage, of any order that is at least unity, between a random variable and a growing set o…
A Case for Maximal Leakage as a Side Channel Leakage Metric
Benjamin Wu, Aaron B. Wagner, G. Edward Suh
Side channels represent a broad class of security vulnerabilities that have been demonstrated to exist in many applications. Because completely eliminating side channels often lead…
MGX: Near-Zero Overhead Memory Protection for Data-Intensive Accelerators
Weizhe Hua, Muhammad Umar, Zhiru Zhang +1
This paper introduces MGX, a near-zero overhead memory protection scheme for hardware accelerators. MGX minimizes the performance overhead of off-chip memory encryption and integri…
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
Yichi Zhang, Ritchie Zhao, Weizhe Hua +3
We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and o…