32 citations · 39 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks
Dake Chen, Christine Goins, Maxwell Waugaman +2
In this paper, we describe and analyze an island-based random dynamic voltage scaling (iRDVS) approach to thwart power side-channel attacks. We first analyze the impact of the numb…
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…