1 citations · 1 across the 11 of their papers we have counts for
11 papers
ROBBIN: Rowhammer-Based Backdoor Injection during Inference
Saion K. Roy, Yufei Wang, A. Adam Ding +1
Existing Rowhammer-based inference-time backdoor attacks design their bit-flip strategies purely at the algorithmic level, without accounting for the bit-flips that the underlying…
SLAC: Access-Driven CPU-to-GPU Side-channel Attacks via System-Level Cache on Apple Silicon
Tianhong Xu, Saion K. Roy, Ruyi Ding +2
Modern heterogeneous System-on-Chip designs integrate CPU cores and a GPU that share a last-level cache (LLC) or system-level cache (SLC). This sharing exposes a new cross-domain a…
MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
Ruyi Ding, Tianhong Xu, Xinyi Shen +2
The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multimodal…
EXAM: Exploiting Exclusive System-Level Cache in Apple M-Series SoCs for Enhanced Cache Occupancy Attacks
Tianhong Xu, Aidong Adam Ding, Yunsi Fei
Cache occupancy attacks exploit the shared nature of cache hierarchies to infer a victim's activities by monitoring overall cache usage, unlike access-driven cache attacks that foc…
USBSnoop -- Revealing Device Activities via USB Congestions
Davis Ranney, Yufei Wang, A. Adam Ding +1
The USB protocol has become a ubiquitous standard for connecting peripherals to computers, making its security a critical concern. A recent research study demonstrated the potentia…
MACPruning: Dynamic Operation Pruning to Mitigate Side-Channel DNN Model Extraction
Ruyi Ding, Cheng Gongye, Davis Ranney +2
As deep learning gains popularity, edge IoT devices have seen proliferating deployment of pre-trained Deep Neural Network (DNN) models. These DNNs represent valuable intellectual p…
Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environment
Ruyi Ding, Tianhong Xu, Aidong Adam Ding +1
Wide deployment of machine learning models on edge devices has rendered the model intellectual property (IP) and data privacy vulnerable. We propose GNNVault, the first secure Grap…
Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing
Ruyi Ding, Tong Zhou, Lili Su +3
Adapting pre-trained deep learning models to customized tasks has become a popular choice for developers to cope with limited computational resources and data volume. More specific…
Non-transferable Pruning
Ruyi Ding, Lili Su, Aidong Adam Ding +1
Pretrained Deep Neural Networks (DNNs), developed from extensive datasets to integrate multifaceted knowledge, are increasingly recognized as valuable intellectual property (IP). T…
GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
Shijin Duan, Ruyi Ding, Jiaxing He +3
Graph-structured data is integral to many applications, prompting the development of various graph representation methods. Graph autoencoders (GAEs), in particular, reconstruct gra…
EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Ruyi Ding, Cheng Gongye, Siyue Wang +2
Deep Neural Networks (DNN) are vulnerable to adversarial perturbations-small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attac…