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Privacy-enhanced multi-region data aggregation for Internet of Vehicles
Vehicular Communications ( IF 5.8 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.vehcom.2025.100886
Haibin Dai, Yuanyuan Zhang, Li Lin, Jinbo Xiong, Youliang Tian

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%.

中文翻译:


面向 Internet of Vehicles 的隐私增强型多区域数据聚合



数据聚合正在演变为促进车联网 (IoV) 决策的极其关键的作用。多区域数据是车联网的典型属性,包含敏感信息和驱动轨迹。但是,现有的隐私保护方案存在区域统计、消息完整性、串通攻击等问题。为了应对这一挑战,我们提出了一种隐私增强的车联网多区域数据聚合 (PRDA) 方案。具体来说,PRDA 通过多密钥共享将传感数据和位置作为掩码值进行保护。我们设计区域向量,使用对称二元多项式生成掩码键,无需交互。此外,车辆会自发生成可验证和可聚合的签名,以确保不安全通信网络中的消息完整性。双线性配对的批量验证可以提高效率,同时抵御恶意对手的篡改攻击。实验表明,随着区域数量的增加,与现有工作相比,PRDA 具有更低的通信开销,并将计算成本降低 32.6% 以上。
更新日期:2025-01-17
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