25 citations · 61 across the 28 of their papers we have counts for
28 papers · 1 filter
SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing
Shashie Dilhara Batan Arachchige, Robin Carpentier, Hassan Jameel Asghar +1
User prompts provided to large language models (LLMs) may contain sensitive or private information that can be misused by remotely deployed models, such as through inadvertent memo…
Protecting User Prompts Via Character-Level Differential Privacy
Shashie Dilhara Batan Arachchige, Hassan Jameel Asghar, Benjamin Zi Hao Zhao +2
Large Language Models (LLMs) generate responses based on user prompts. Often, these prompts may contain highly sensitive information, including personally identifiable information…
CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
Shashie Dilhara Batan Arachchige, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +2
Large Language Models (LLMs) are often fine-tuned to adapt their general-purpose knowledge to specific tasks and domains such as cyber threat intelligence (CTI). Fine-tuning is mos…
Privacy-Preserving IoT in Connected Aircraft Cabin
Nilesh Vyas, Benjamin Zhao, Aygün Baltaci +6
The proliferation of IoT devices in shared, multi-vendor environments like the modern aircraft cabin creates a fundamental conflict between the promise of data collaboration and th…
VWAttacker: A Systematic Security Testing Framework for Voice over WiFi User Equipments
Imtiaz Karim, Hyunwoo Lee, Hassan Asghar +4
We present VWAttacker, the first systematic testing framework for analyzing the security of Voice over WiFi (VoWiFi) User Equipment (UE) implementations. VWAttacker includes a comp…
A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem
Sunday Oyinlola Ogundoyin, Muhammad Ikram, Hassan Jameel Asghar +2
Millions of users leverage generative pretrained transformer (GPT)-based language models developed by leading model providers for a wide range of tasks. To support enhanced user in…