A publisher who releases check-in trajectories inadvertently publishes a strong predictor of every user's future locations. We address this risk by generating unlearnable trajectories, perturbed sequences that yield victim models with degraded next-Point-of-Interest (next-POI) accuracy on clean test inputs. Direct ports of image-domain unlearnable examples fail on two counts. The published data must remain geographically and semantically plausible, and the perturbation must resist purification adversaries that exploit the structure of randomized defences. We propose Ghost, a manifold-aligned framework whose perturbations look like plausible human check-in sequences yet leave no learnable signal behind. Ghost steers each substitution onto the real-trajectory manifold through a frozen trajectory language model, so a denoising-bridge adversary has nothing to invert and a context-free frequency-table adversary recovers a near-uniform distribution. Across two standard benchmarks, and four attacker postures, Ghost achieves protection-gap competitive with the strongest deterministic baseline (PGD) while attaining the lowest restored accuracy under the bigram adaptive purification adversary on both datasets, and lies within one per-cell standard deviation of PGD on the protection-versus-purification-resistance plane. Ablations confirm the manifold prior subsumes the entropy-floor knob of prior randomized defences, with the frequency-table adversary's survival gap remaining within 0.04 even when twenty percent of the pairs are leaked.

翻译：发布签到轨迹的数据发布者在无意中为每个用户的未来位置提供了强预测器。我们通过生成不可学习轨迹（即扰动后的序列）来应对这一风险，这些序列能使受害模型在干净测试输入上的下一兴趣点（next-POI）预测精度下降。图像领域的不可学习示例直接移植存在两个问题：发布数据必须保持地理与语义上的似真性，且扰动必须能抵御利用随机防御结构进行净化的攻击者。我们提出Ghost——一种流形对齐框架，其扰动看似人类签到序列却未留下可学习信号。Ghost通过冻结的轨迹语言模型将每次替换引导至真实轨迹流形上，使得去噪桥接攻击者无从逆向，且无上下文频率表攻击者恢复出接近均匀分布。在两个标准基准测试和四种攻击模式下，Ghost的保护差距与最强确定性基线（PGD）相当，在两种数据集上均达到二元自适应净化攻击者下的最低恢复精度，并在保护-抗净化权衡平面上与PGD相差一个单元格标准差以内。消融实验证实，流形先验吸收了先前随机防御的熵底旋钮，即使泄露百分之二十的二元组，频率表攻击者的生存差距仍保持在0.04以内。