Assessing Membership Leakages via Task-Aligned Divergent Shadow Datasets in Vehicular Road Cooperation
Deep classification models have been widely utilized in Vehicular Road Cooperation.
However, previous work indicates that deep classification models are vulnerable to the …
However, previous work indicates that deep classification models are vulnerable to the …
PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving
Massive data collected on public roads for autonomous driving has become more popular in
many locations in the world. More collected data leads to more concerns about data privacy …
many locations in the world. More collected data leads to more concerns about data privacy …
Privacy preserving misbehavior detection in IoV using federated machine learning
Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles
(IoV) is achieved by corrupting the data exchanged between nodes with false information …
(IoV) is achieved by corrupting the data exchanged between nodes with false information …
Privacy preserving machine learning for electric vehicles: A survey
In the recent years, the interest of individual users in modern electric vehicles (EVs) has
grown exponentially. An EV has two major components, which make it different from …
grown exponentially. An EV has two major components, which make it different from …
Federated learning to enable automotive collaborative ecosystem: opportunities and challenges
Despite the strong interests in creating data economy, automotive industries are creating
data silos with each stakeholder maintaining its own data cloud. Federated learning (FL) …
data silos with each stakeholder maintaining its own data cloud. Federated learning (FL) …
Federated learning with differential privacy for resilient vehicular cyber physical systems
Vehicular cyber physical systems (VCPS) will play a vital role in the quest to develop
intelligent transportation systems (ITS) and smart cities around the world. Consequently …
intelligent transportation systems (ITS) and smart cities around the world. Consequently …
Secure intrusion detection by differentially private federated learning for inter-vehicle networks
Q Xu, L Zhang, D Ou, W Yu - Transportation research record, 2023 - journals.sagepub.com
Along with providing several benefits, the unprecedented growth of connected and
automated vehicles brings worries about damaging cyber attacks. Network-based intrusion …
automated vehicles brings worries about damaging cyber attacks. Network-based intrusion …
Beyond model-level membership privacy leakage: an adversarial approach in federated learning
With the rise of privacy concerns in traditional centralized machine learning services, the
federated learning, which incorporates multiple participants to train a global model across …
federated learning, which incorporates multiple participants to train a global model across …
Feel: Federated end-to-end learning with non-iid data for vehicular ad hoc networks
Recent studies have demonstrated the potentials of federated learning (FL) in achieving
cooperative and privacy-preserving data analytics. It would also be promising if FL can be …
cooperative and privacy-preserving data analytics. It would also be promising if FL can be …
Federated Split Learning with Data and Label Privacy Preservation in Vehicular Networks
Federatedlearning (FL) is an emerging distributed learning paradigm widely used in
vehicular networks, where vehicles are enabled to train the deep model for the server while …
vehicular networks, where vehicles are enabled to train the deep model for the server while …