activity
20172026
most citedDarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments

190 citations · 289 across the 21 of their papers we have counts for

collaborators
Showing cs.LGShow all

10 papers · 1 filter

cs.LG2026

AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

Dzung Pham, Kleomenis Katevas, Ali Shahin Shamsabadi +1

Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote,…

cs.LG2025

Membership and Memorization in LLM Knowledge Distillation

Ziqi Zhang, Ali Shahin Shamsabadi, Hanxiao Lu +2

Recent advances in Knowledge Distillation (KD) aim to mitigate the high computational demands of Large Language Models (LLMs) by transferring knowledge from a large ''teacher'' to…

cs.LG2022

Private Multi-Winner Voting for Machine Learning

Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6

Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…

cs.LG2022

On the reversibility of adversarial attacks

Chau Yi Li, Ricardo Sánchez-Matilla, Ali Shahin Shamsabadi +2

Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of…

cs.LG2022★ 7 cited

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet +1

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Pert…

cs.LG2022

Tubes Among Us: Analog Attack on Automatic Speaker Identification

Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi +4

Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial…