A secure and lightweight privacy-preserving data aggregation scheme for internet of vehicles

Data aggregation can effectively improve resource utilization in Internet of Vehicles (IoV), and has attracted broad attention recently. However, because of the particularity of nodes in IoV, data aggregation faces security challenges such as location privacy, data privacy and accuracy of aggregation results. To solve these challenges, in this paper, we propose a secure and lightweight privacy-preserving data aggregation scheme, named SLPDA, for IoV. Specifically, SLPDA employs Chinese Remainder Theorem to map multi-dimensional data to one-dimensional data, and uses masking technology to encrypt data, thus roadside units can realize lightweight data aggregation without plaintext. SLPDA also adopts Identity-based batch authentication technology to reduce authentication overhead. Detailed security analysis indicates that SLPDA can resist various security threats. In addition, the performance evaluations demonstrate that SLPDA is more efficient than the reported schemes in terms of computation complexity and communication overhead.