3 citations · 9 across the 7 of their papers we have counts for
7 papers
Machine Learning with Privacy for Protected Attributes
Saeed Mahloujifar, Chuan Guo, G. Edward Suh +1
Differential privacy (DP) has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes…
How much do language models memorize?
John X. Morris, Chawin Sitawarin, Chuan Guo +5
We propose a new method for estimating how much a model knows about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memori…
Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilities
Evan Lai, Wenjie Xiong, Edward Suh +2
Speculative attacks such as Spectre can leak secret information without being discovered by the operating system. Speculative execution vulnerabilities are finicky and deep in the…
Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference
Michael Shen, Muhammad Umar, Kiwan Maeng +2
The rapid increase in the number of parameters in large language models (LLMs) has significantly increased the cost involved in fine-tuning and retraining LLMs, a necessity for kee…
Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction
Sai Qian Zhang, Ziyun Li, Chuan Guo +5
Inverting visual representations within deep neural networks (DNNs) presents a challenging and important problem in the field of security and privacy for deep learning. The main go…
FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
Jiongxiao Wang, Fangzhou Wu, Wendi Li +5
Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external informa…