32 citations · 41 across the 11 of their papers we have counts for
11 papers
Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis
Jacob Huckelberry, Andrea Mattia Garavagno, Yuke Zhang +3
Most TinyML hardware accelerators focus on supporting Quantized Neural Networks (QNNs) to meet stringent constraints on power consumption and size. Despite this, the security aspec…
FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings
Jingqi Xu, Chenghao Li, Yuke Zhang +1
Diffusion models have demonstrated remarkable potential in generating high-quality images. However, their tendency to replicate training data raises serious privacy concerns, parti…
LoyalDiffusion: A Diffusion Model Guarding Against Data Replication
Chenghao Li, Yuke Zhang, Dake Chen +2
Diffusion models have demonstrated significant potential in image generation. However, their ability to replicate training data presents a privacy risk, particularly when the train…
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Jacob Huckelberry, Yuke Zhang, Allison Sansone +3
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique securit…
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
Chenghao Li, Dake Chen, Yuke Zhang +1
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent…
Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP
Dake Chen, Xuan Zhou, Yinghua Hu +5
Logic locking has become a promising approach to provide hardware security in the face of a possibly insecure fabrication supply chain. While many techniques have focused on lockin…
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Souvik Kundu, Yuke Zhang, Dake Chen +1
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimiza…
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference
Yuke Zhang, Dake Chen, Souvik Kundu +3
Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer fro…
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference
Souvik Kundu, Shunlin Lu, Yuke Zhang +2
The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce…
TriLock: IC Protection with Tunable Corruptibility and Resilience to SAT and Removal Attacks
Yuke Zhang, Yinghua Hu, Pierluigi Nuzzo +1
Sequential logic locking has been studied over the last decade as a method to protect sequential circuits from reverse engineering. However, most of the existing sequential logic l…
Fun-SAT: Functional Corruptibility-Guided SAT-Based Attack on Sequential Logic Encryption
Yinghua Hu, Yuke Zhang, Kaixin Yang +3
The SAT attack has shown to be efficient against most combinational logic encryption methods. It can be extended to attack sequential logic encryption techniques by leveraging circ…