The Domain Name System (DNS) is an essential component of Internet infrastructure but faces significant challenges in scalability, security, and centralized control. Traditional centralized architectures are limited by single points of failure and inefficiencies in cross-chain data synchronization management. In response, this paper introduces Cross-DNS, a consortium blockchain-based framework designed to improve DNS resolution through a hierarchical cross-chain architecture. This architecture employs a mainchain as the central coordination platform and multiple sidechains to effectively manage decentralized domain synchronization. Notable innovations include a two-phase voting and verification protocol to maintain transaction integrity, a reputation-based committee election system to prevent malicious and lazy behaviors, and enhanced cross-chain query methods to ensure timely resolution. Our experimental evaluations confirm that Cross-DNS effectively increases transaction processing capacity and resolution speed, while controlling the risks associated with centralized control of root servers. By integrating blockchain’s decentralization advantages with DNS operational demands, this framework makes significant progress in DNS scalability and addresses key issues such as compatibility with existing systems.

With the globalized deployment of cross-border vehicle location services and the trajectory data, which contain user identity information and geographically sensitive features, the variability in privacy regulations in different jurisdictions can further exacerbate the technical and compliance challenges of data privacy protection. Traditional static differential privacy mechanisms struggle to accommodate spatiotemporal heterogeneity in dynamic scenarios because of the use of a fixed privacy budget parameter, leading to wasted privacy budgets or insufficient protection of sensitive regions. This study proposes a reinforcement-learning-based dynamic noise optimization method (DNO-RL) that dynamically adjusts the Laplacian noise scale by real-time sensing of vehicle density, region sensitivity, and the remaining privacy budget via a deep Q-network (DQN), with the aim of providing context-adaptive differential privacy protection for cross-border vehicle location services. Simulation experiments of cross-border scenarios based on the T-Drive dataset showed that DNO-RL reduced the average localization error by 28.3% and saved 17.9% of the privacy budget compared with the local differential privacy under the same privacy budget. This study provides a new paradigm for the dynamic privacy–utility balancing of cross-border vehicular networking services.