Privacy-enhanced multi-region data aggregation for Internet of Vehicles

Abstract

Data aggregation is evolving into an extremely crucial role for facilitating decision-making in Internet of Vehicles (IoV). Multi-region data is a typical attribute of IoV, containing sensitive information and driving trajectories. However, the existing privacy-preserving schemes face problems such as regional statistics, messages integrity, and collusion attacks. In order to tackle this challenge, we propose a privacy-enhanced multi-region data aggregation (PRDA) scheme for IoV. Specifically, PRDA protects both sensing data and location as masked values by multi-secret sharing. We design regional vectors to generate mask keys using symmetric bivariate polynomial without interaction. In addition, vehicles spontaneously generate verifiable and aggregatable signature to ensure messages integrity in insecure communication networks. Batch verification of bilinear pairing can improve efficiency while resisting tampering attacks by malicious adversaries. Experiments demonstrate that as the number of regions increases, comparing with existing works, PRDA has lower communication overhead, and decreases computational cost by over 32.6%.