1 citations · 2 across the 11 of their papers we have counts for
12 papers
When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels
Parnian Fazel, Mohammad M Maheri, Hamed Haddadi
Approximate unlearning answers a deletion request on a knowledge-graph embedding by editing entity embeddings instead of retraining from scratch. These embeddings keep being update…
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…
ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs
Mohammad M Maheri, Sunil Cotterill, Alex Davidson +1
Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers…
WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
Mohammad M Maheri, Xavier Cadet, Peter Chin +1
Approximate machine unlearning aims to efficiently remove the influence of specific data points from a trained model, offering a practical alternative to full retraining. However,…
GuardNet: Graph-Attention Filtering for Jailbreak Defense in Large Language Models
Javad Forough, Mohammad Maheri, Hamed Haddadi
Large Language Models (LLMs) are increasingly susceptible to jailbreak attacks, which are adversarial prompts that bypass alignment constraints and induce unauthorized or harmful b…
Verifiable Unlearning on Edge
Mohammad M Maheri, Alex Davidson, Hamed Haddadi
Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infrin…
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning
Mohammad Mahdi Maheri, Denys Herasymuk, Hamed Haddadi
The growing adoption of Artificial Intelligence (AI) in Internet of Things (IoT) ecosystems has intensified the need for personalized learning methods that can operate efficiently…
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
Mohammad M Maheri, Hamed Haddadi, Alex Davidson
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires acce…
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…
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
Mina Rafiei, Mohammadmahdi Maheri, Hamid R. Rabiee
Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally lea…
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…