  The emerging trends of Internet-of-Vehicles (IoV) demand centralized servers
to collect/process sensitive data with limited computational resources on a
single vehicle. Such centralizations of sensitive data demand practical privacy
protections. One widely-applied paradigm, Differential Privacy, can provide
strong guarantees over sensitive data by adding noises. However, directly
applying DP for IoV incurs significant challenges for data utility and
effective protection. We observe that the key issue about DP-enabled protection
in IoV lies in how to synergistically combine DP with special characteristics
of IoV, whose query sequences are usually formed as unbalanced batches due to
frequent interactions between centralized servers and edge vehicles.
  To this end, we propose HUT, a new algorithm to enable High UTility for
DP-enabled protection in IoV. Our key insight is to leverage the inherent
characteristics in IoV: the unbalanced batches. Our key idea is to aggregate
local batches and apply Order Constraints, so that information loss from DP
protection can be mitigated. We evaluate the effectiveness of HUT against the
state-of-the-art DP protection mechanisms. The results show that HUT can
provide much lower information loss by 95.69\% and simultaneously enable strong
mathematically-guaranteed protection over sensitive data.
