The rapid advancement of location-based services (LBSs) in three-dimensional (3D) domains, such as smart cities and intelligent transportation, has raised concerns over 3D spatiotemporal trajectory privacy protection. However, existing research has not fully addressed the risk of attackers exploiting the spatiotemporal correlation of 3D spatiotemporal trajectories and the impact of height information, both of which can potentially lead to significant privacy leakage. To address these issues, this paper proposes a personalized 3D spatiotemporal trajectory privacy protection mechanism, named 3DSTPM. First, we analyze the characteristics of attackers that exploit spatiotemporal correlations between locations in a trajectory and present the attack model. Next, we exploit the complementary characteristics of 3D geo-indistinguishability (3D-GI) and distortion privacy to find a protection location set (PLS) that obscures the real location for all possible locations. To address the issue of privacy accumulation caused by continuous trajectory queries, we propose a Window-based Adaptive Privacy Budget Allocation (W-APBA), which dynamically allocates privacy budgets to all locations in the current PLS based on their predictability and sensitivity. Finally, we perturb the real location using the allocated privacy budget by the PF (Permute-and-Flip) mechanism, effectively balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that the proposed 3DSTPM effectively reduces QoS loss while meeting the user's personalized privacy protection needs.

翻译：随着三维智能城市和智能交通等领域中基于位置服务的快速发展，三维时空轨迹隐私保护问题日益受到关注。然而，现有研究尚未充分解决攻击者利用三维时空轨迹的时空相关性以及高度信息带来的隐私泄露风险。针对这些问题，本文提出一种名为3DSTPM的个性化三维时空轨迹隐私保护机制。首先，我们分析了攻击者利用轨迹中位置间时空相关性的特征，并提出了相应的攻击模型。接着，利用三维地理不可区分性（3D-GI）与失真隐私的互补特性，寻找能够对所有可能位置隐藏真实位置的保护位置集（PLS）。为应对连续轨迹查询引发的隐私累积问题，提出了一种基于窗口的自适应隐私预算分配方法（W-APBA），该方法根据当前PLS中各位置的可预测性与敏感度动态分配隐私预算。最后，采用排列-翻转机制（PF）基于分配的隐私预算对真实位置进行扰动，有效平衡了隐私保护与服务质量（QoS）。仿真结果表明，所提出的3DSTPM在满足用户个性化隐私保护需求的同时，显著降低了服务质量损失。