55 citations · 80 across the 18 of their papers we have counts for
4 papers · 1 filter
Information Theoretic Adversarial Training of Large Language Models
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors under novel attack strategies.…
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
Kasra Ahmadi, Rouzbeh Behnia, Reza Ebrahimi +4
Federated learning (FL) enhances privacy by keeping user data on local devices. However, emerging attacks have demonstrated that the updates shared by users during training can rev…
From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
Yuncong Yang, Xiao Han, Yidong Chai +3
Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, c…
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
Jeremiah Birrell, Reza Ebrahimi, Rouzbeh Behnia +1
Differentially private stochastic gradient descent (DP-SGD) has been instrumental in privately training deep learning models by providing a framework to control and track the priva…