In air-ground integrated networks (AGINs), unmanned aerial vehicles (UAVs) provide on-demand edge services to ground vehicles. Realizing this vision requires carefully designed incentives to coordinate interactions among self-interested participants. This is exacerbated by the dynamic nature of AGINs, where spatio-temporal variations introduce significant uncertainty in matching UAVs and vehicles. Existing real-time service provisioning typically relies on precise trajectory information, raising privacy concerns and incurring decision latency. To address these challenges, we propose look one-step ahead (LOSA), a novel framework for efficient and privacy-aware service provisioning. By exploiting predictable vehicle travel times between intersections, LOSA decomposes the process into two coupled phases: (i) a privacy-aware look-ahead phase and (ii) a lightweight real-time execution phase. The look-ahead phase allows vehicles to adaptively adjust privacy budgets based on historical utility, balancing trajectory exposure and matching accuracy. Leveraging this, a double auction mechanism establishes binding one-step-ahead agreements (OSAAs) through trajectory similarity clustering, while constructing preference lists to hedge against mobility uncertainty. The execution phase then enforces pre-established OSAAs and preference lists, resolving real-time resource conflicts without costly re-negotiations. This design reduces computational overhead and preserves robustness. We analytically corroborate that LOSA guarantees truthfulness, individual rationality, and budget balance. Experiments on real-world datasets (DAIR-V2X, HighD, and RCooper) demonstrate that LOSA achieves superior privacy protection while lowering transaction latency compared to baseline approaches.

翻译：在空地一体化网络（AGINs）中，无人机（UAVs）为地面车辆提供按需边缘服务。实现这一愿景需要精心设计的激励措施来协调自利参与者之间的交互。AGINs的动态特性加剧了这一挑战——时空变化为无人机与车辆的匹配带来了显著不确定性。现有实时服务提供通常依赖精确轨迹信息，引发隐私问题并导致决策延迟。为解决这些挑战，我们提出LOSA（前瞻一步）框架，这是一种高效且兼顾隐私感知的服务提供新方案。通过利用交叉口间车辆行程时间的可预测性，LOSA将流程分解为两个耦合阶段：（i）隐私感知的前瞻阶段和（ii）轻量级实时执行阶段。在前瞻阶段，车辆可基于历史效用自适应调整隐私预算，平衡轨迹暴露与匹配精度。基于此，双重拍卖机制通过轨迹相似性聚类建立具有约束力的前瞻一步协议（OSAAs），同时构建偏好列表以对冲移动性不确定性。执行阶段则强制执行预先建立的OSAAs和偏好列表，无需昂贵重协商即可解决实时资源冲突。这种设计降低了计算开销并保持了鲁棒性。理论分析证实LOSA满足真实性、个体理性与预算平衡性。在DAIR-V2X、HighD和RCooper真实数据集上的实验表明，与基线方法相比，LOSA在降低交易延时的同时实现了更优的隐私保护。