1 citations · 2 across the 5 of their papers we have counts for
6 papers
AgenTEE: Confidential LLM Agent Execution on Edge Devices
Sina Abdollahi, Mohammad M Maheri, Javad Forough +5
Large Language Model (LLM) agents provide powerful automation capabilities, but they also create a substantially broader attack surface than traditional applications due to their t…
Sharing is caring: Attestable and Trusted Workflows out of Distrustful Components
Amir Al Sadi, Sina Abdollahi, Adrien Ghosn +2
Trusted Execution Environments (TEEs) protect private memory and attest initial code and configuration, but neither constrain nor attest shared channels between TEEs or with the un…
CAEC: Confidential, Attestable, and Efficient Inter-CVM Communication with Arm CCA
Sina Abdollahi, Amir Al Sadi, David Kotz +2
Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation…
An Early Experience with Confidential Computing Architecture for On-Device Model Protection
Sina Abdollahi, Mohammad Maheri, Sandra Siby +2
Deploying machine learning (ML) models on user devices can improve privacy (by keeping data local) and reduce inference latency. Trusted Execution Environments (TEEs) are a practic…
P4: Towards private, personalized, and Peer-to-Peer learning
Mohammad Mahdi Maheri, Sandra Siby, Sina Abdollahi +2
Personalized learning is a proposed approach to address the problem of data heterogeneity in collaborative machine learning. In a decentralized setting, the two main challenges of…
GuaranTEE: Towards Attestable and Private ML with CCA
Sandra Siby, Sina Abdollahi, Mohammad Maheri +2
Machine-learning (ML) models are increasingly being deployed on edge devices to provide a variety of services. However, their deployment is accompanied by challenges in model priva…