5 papers · 1 filter
Defense Against Prompt Inversion Attacks: An Information-Theoretic Approach for LLM Collaborative Inference
Sayedeh Leila Noorbakhsh, Hossein Khalili, Nader Sehatbakhsh
Collaborative edge-cloud inference enables resource-constrained devices to leverage large language models (LLMs) by offloading partial computation to cloud servers. However, transm…
To See or Not to See -- Fingerprinting Devices in Adversarial Environments Amid Advanced Machine Learning
Justin Feng, Amirmohammad Haddad, Nader Sehatbakhsh
The increasing use of the Internet of Things raises security concerns. To address this, device fingerprinting is often employed to authenticate devices, detect adversaries, and ide…
LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models
Hossein Khalili, Seongbin Park, Vincent Li +4
Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learnin…
SoK: A Study of the Security on Voice Processing Systems
Robert Chang, Logan Kuo, Arthur Liu +1
As the use of Voice Processing Systems (VPS) continues to become more prevalent in our daily lives through the increased reliance on applications such as commercial voice recogniti…
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
Nader Sehatbakhsh, Ellie Daw, Onur Savas +2
As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by r…