  Internet of Vehicles (IoV) is a promising branch of the Internet of Things.
IoV simulates a large variety of crowdsourcing applications such as Waze, Uber,
and Amazon Mechanical Turk, etc. Users of these applications report the
real-time traffic information to the cloud server which trains a machine
learning model based on traffic information reported by users for intelligent
traffic management. However, crowdsourcing application owners can easily infer
users' location information, which raises severe location privacy concerns of
the users. In addition, as the number of vehicles increases, the frequent
communication between vehicles and the cloud server incurs unexpected amount of
communication cost. To avoid the privacy threat and reduce the communication
cost, in this paper, we propose to integrate federated learning and local
differential privacy (LDP) to facilitate the crowdsourcing applications to
achieve the machine learning model. Specifically, we propose four LDP
mechanisms to perturb gradients generated by vehicles. The Three-Outputs
mechanism is proposed which introduces three different output possibilities to
deliver a high accuracy when the privacy budget is small. The output
possibilities of Three-Outputs can be encoded with two bits to reduce the
communication cost. Besides, to maximize the performance when the privacy
budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We
further propose a suboptimal mechanism (PM-SUB) with a simple formula and
comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by
combining Three-Outputs and PM-SUB.
