Presentation
Defending Membership Inference Attack on Edge using Trusted Execution Environments
DescriptionPrivacy concerns arise from malicious attacks on Deep Neural Network (DNN) applications during sensitive data inference on edge devices. Our proposed defense method addresses limitations in existing Trusted Execution Environments (TEEs) by employing depth-wise layer partitioning for large DNNs and a model quantization strategy. This enhances protection against both white-box and black-box Membership Inference Attacks (MIAs) while accelerating computation. Experiments on Raspberry Pi 3B+ demonstrate significant reductions in white-box MIA accuracy (up to 35.3%) and black-box MIA accuracy (up to 29.6%) for popular DNN models (AlexNet, VGG-16, ResNet-20) on CIFAR-100 dataset.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security