2 citations · 2 across the 2 of their papers we have counts for
6 papers
RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi +1
Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce m…
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.…
Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz +2
Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy at…
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…
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
Yiwei Zhang, Rouzbeh Behnia, Attila A. Yavuz +2
Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server…