1 citations · 4 across the 4 of their papers we have counts for
6 papers
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…
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…
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…