2 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
Tong Zhou, Jiahui Zhao, Yukui Luo +4
Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, exi…
Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
Tong Zhou, Xuandong Zhao, Xiaolin Xu +1
Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when comba…
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
Ziyu Liu, Tong Zhou, Yukui Luo +1
Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, the existing solutions either provide inadequate protection or…