On Vehicular Data Aggregation in Federated Learning: A Case Study of Privacy with Parking Occupancy in Eclipse SUMO

Levente Alekszejenkó; Tadeusz Dobrowiecki

doi:10.52825/scp.v5i.1100

Authors

Levente Alekszejenkó Budapest University of Technology and Economics https://orcid.org/0000-0002-3196-1950
Tadeusz Dobrowiecki Budapest University of Technology and Economics https://orcid.org/0000-0002-8307-5096

DOI:

https://doi.org/10.52825/scp.v5i.1100

Keywords:

Vehicular Federated Learning, Location Privacy, Deep Leakage From Gradients

Abstract

Vehicular federated learning systems will be beneficial to predicting traffic events in future intelligent cities. However, they might leak private information upon model updates. Hence, an honest but curious server could infer private information, such as the route of a vehicle. In this study, we elaborate on the nature of such privacy leakage caused by gradient sharing. With a simulated scenario, we focus on determining who is in danger of privacy threats and how successful a route inference attack can be.

Results indicate that vanilla federated learning exposes intra-city and commuter traffic to successful location inference attacks. We also found that an adversarial aggregator server successfully infers the moving time of vehicles traveling during low-traffic periods.

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On Vehicular Data Aggregation in Federated Learning

A Case Study of Privacy with Parking Occupancy in Eclipse SUMO

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