24 citations · 60 across the 13 of their papers we have counts for
13 papers
The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah
Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Perso…
AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah
Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure. When agents coordinate on tasks, sensitive data may pass thr…
Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
Godwin Badu-Marfo, Bilal Farooq
We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns…
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems
Godwin Badu-Marfo, Ranwa Al Mallah, Bilal Farooq
The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were…
Robustness Analysis of Deep Learning Models for Population Synthesis
Daniel Opoku Mensah, Godwin Badu-Marfo, Bilal Farooq
Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset an…
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
Daniel Opoku Mensah, Godwin Badu-Marfo, Ranwa Al Mallah +1
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private informati…
On the Initial Behavior Monitoring Issues in Federated Learning
Ranwa Al Mallah, Godwin Badu-Marfo, Bilal Farooq
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks ai…
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems
Ranwa Al Mallah, Godwin Badu-Marfo, Bilal Farooq
Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks…
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation
Ranwa Al Mallah, David Lopez, Godwin Badu Marfo +1
Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues by training a glo…
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches. This propose…
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
Godwin Badu-Marfo, Bilal Farooq, Zachary Paterson
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entir…
Perturbation Methods for Protection of Sensitive Location Data: Smartphone Travel Survey Case Study
Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
Smartphone based travel data collection has become an important tool for the analysis of transportation systems. Interest in sharing travel survey data has gained popularity in rec…
A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data
Godwin Badu-Marfo, Bilal Farooq, Zachary Patterson
In recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potenti…