Schnorr Approval-Based Secure and Privacy-Preserving IoV Data Aggregation
CoRR(2024)
摘要
Secure and privacy-preserving data aggregation in the Internet of Vehicles
(IoV) continues to be a focal point of interest in both the industry and
academia. Aiming at tackling the challenges and solving the remaining
limitations of existing works, this paper introduces a novel Schnorr
approval-based IoV data aggregation framework based on a two-layered
architecture. In this framework, a server can aggregate the IoV data from
clusters without inferring the raw data, real identity and trajectories of
vehicles. Notably, we avoid incorporating the widely-accepted techniques such
as homomorphic encryption and digital pseudonym to avoid introducing high
computation cost to vehicles. We propose a novel concept, data approval, based
on the Schnorr signature scheme. With the approval, the fake data injection
attack carried out by a cluster head can be defended against. The separation of
liability is achieved as well. The evaluation shows that the framework is
secure and lightweight for vehicles in terms of the computation and
communication costs.
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