# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 3 Internet of Vehicles
### Differential Privacy on Internet of Vehicles  
In the literature on IoV, differential privacy has often been applied for the protection of trajectory data and location privacy. [PERSON] et al. [45] develop a geographically indistinguishable method that leverages differential privacy to obscure a vehicle's location within a specific range. [PERSON] et al. [46] propose a location privacy-preserving method in vehicle-based crowdsensing networks. Each vehicle obfuscates its own location data using differential privacy and then submits them to an application platform. [PERSON] et al. [47] adopt differential privacy when releasing a vehicle's trajectory data. It uses a dynamic sampling method to process the data and a privacy budget allocation method for location protection. [PERSON] et al. [48] propose a method of releasing a trajectory using a differential privacy dataset in IoV. The method constructs a noisy prefix tree based on the spatiotemporal characteristics of the trajectory data and utilizes a Markov chain to minimize the cost of adding noise. [PERSON] et al. [49] present a quadtree-based spatial decomposition algorithm to protect the location privacy of electric vehicles in a vehicle-to-grid network. The algorithm leverages a quadtree and Bernoulli random sampling to enhance relative error with respect to the privacy budget. [PERSON] et al. [50] propose a method of synthesizing private and realistic trajectory data by using a geography-aware grid that integrates the public geography structures of a target area.  
In another side of the literature, differential privacy has been used in IoV for general purposes. [PERSON] et al. [38] investigate the integration of federated learning with differential privacy to enable crowdsourcing applications for both privacy and network efficiency. [PERSON] et al. [51] propose a two-layer framework for private data sharing in vehicular networks. Each vehicle adds noise to local raw data and then forwards them to nearby a roadside unit that is responsible for training a local model with the perturbed data. The unit then uploads the trained parameters to a coordinating server. [PERSON] et al. [52] apply both federated learning and differential privacy to connected vehicles to make vehicular cyber-physical systems resilient to adversarial attacks. To enhance resiliency, they use a layer-wise relevance propagation method that can regulate the selection of perturbation values. [PERSON] et al. [53] investigate a privacy-preserved parking recommender system. They propose a mutual authentication mechanism using elliptic curve cryptography and make use of differential privacy to perturb data when sharing users' historical parking data with third-party recommender systems. [PERSON] et al. [54] summarize privacy-preserving techniques including differential privacy applied to electric vehicles.  
There is a very short list of research that applied both _federated learning_ and _differential privacy_ to the Internet of Vehicles. To the best of the authors' knowledge, few prior studies have applied them in an environment considering _mobility patterns_ and evaluated the impact of mobility on their performance, which are the final goals of this work.