Channel charting enables location-based services (LBSs) without requiring explicit position information by using pseudo-locations from the channel chart. While this property implies inherent privacy advantages, it does not provide formal privacy guarantees. In this work, we address location privacy in channel charting referred to as chart location indistinguishability (CLI), which extends geo-indistinguishability (GI) to channel charting representations. In order to achieve CLI, a standard planar Laplace mechanism is investigated and a geometry-aware Mahalanobis norm planar Laplace (MNPL) mechanism is devised. The proposed MNPL mechanism perturbs the channel chart by injecting noise aligned with the local structure of the chart. In the CLI framework with MNPL, privacy is defined in latent channel chart manifolds using locally adaptive covariance derived from chart neighborhoods, while preserving manifold topology under privacy constraints. In addition, differential privacy is considered as a privacy baseline. The proposed approach is evaluated across multiple channel charting schemes. The performance is assessed using utility metrics such as quality loss (QL) and range query error (RQE), as well as geometry-aware metrics including trustworthiness (TW) and continuity (CT). Numerical results demonstrate that the proposed privacy mechanism provides strong privacy guarantees while preserving the channel chart for LBSs tasks.

翻译：信道图制图通过利用信道图中的伪位置实现基于位置的服务（LBSs），而无需显式的位置信息。尽管这一特性隐含了固有的隐私优势，但它并未提供形式化的隐私保证。在这项工作中，我们研究了信道图制图中的位置隐私问题，称为图表位置不可区分性（CLI），它将地理不可区分性（GI）扩展到信道图表示。为了实现CLI，我们研究了标准平面拉普拉斯机制，并提出了一种几何感知的基于马氏范数的平面拉普拉斯（MNPL）机制。所提出的MNPL机制通过注入与图表局部结构对齐的噪声来扰动信道图。在基于MNPL的CLI框架中，隐私定义在潜在的信道图流形上，利用从图表邻域导出的局部自适应协方差，同时在隐私约束下保持流形拓扑。此外，差分隐私被作为隐私基准。所提出的方法在多种信道图制图方案上进行了评估。性能评估使用了质量损失（QL）和范围查询误差（RQE）等效用度量，以及可信度（TW）和连续性（CT）等几何感知度量。数值结果表明，所提出的隐私机制在保持信道图以支持LBSs任务的同时，提供了强大的隐私保证。