Sparse Mobile CrowdSensing (SMCS) effectively lowers sensing costs while maintaining data quality, offering an alternative approach to data collection. Unfortunately, the fact that data contain sensitive information raises serious privacy concerns. Local Differential Privacy (LDP) has emerged as the de facto standard for ensuring data privacy. However, the LDP based on the perturbation concept causes a substantial reduction in the data utility of the SMCS system. To address this problem, we propose a novel scheme named enhancing Sparse mobile crowdsensing With manifold Optimization and differential Privacy (SWOP). Specifically, we first revisit the Gaussian mechanism based on the fact that data utility intervals are ubiquitous in sensing tasks, and introduce a novel perturbation mechanism, namely Truncated Gaussian Mechanism (TGM). Subsequently, we perturb user-collected data by locally injecting noise sampled from TGM and deduce a sufficient condition for the scale parameter to ensure _\epsilon -LDP. Furthermore, we model the data inference with privacy-preserving properties as an unconstrained optimization problem on a Riemannian manifold and solve it using the nonlinear conjugate gradient method. Extensive experiments on large-scale real-world and synthetic datasets are conducted to evaluate the proposed scheme. The results demonstrate that SWOP can greatly enhance the utility of data inference while ensuring workers’ data privacy compared to baseline models.

中文翻译：

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通过流形优化和差异隐私增强稀疏移动群体感知


稀疏移动人群感知 (SMCS) 可有效降低传感成本，同时保持数据质量，提供另一种数据收集方法。不幸的是，数据包含敏感信息这一事实引起了严重的隐私问题。本地差分隐私 (LDP) 已成为确保数据隐私的事实上的标准。然而，基于扰动概念的LDP导致SMCS系统的数据效用大幅降低。为了解决这个问题，我们提出了一种名为通过流形优化和差分隐私增强稀疏移动群智感知（SWOP）的新方案。具体来说，我们首先基于数据效用区间在传感任务中普遍存在的事实重新审视高斯机制，并引入一种新颖的扰动机制，即截断高斯机制（TGM）。随后，我们通过本地注入从 TGM 采样的噪声来扰乱用户收集的数据，并推导出尺度参数的充分条件以确保 _\epsilon -LDP。此外，我们将具有隐私保护特性的数据推理建模为黎曼流形上的无约束优化问题，并使用非线性共轭梯度法对其进行求解。对大规模现实世界和合成数据集进行了广泛的实验来评估所提出的方案。结果表明，与基线模型相比，SWOP 可以极大地增强数据推理的效用，同时确保工作人员的数据隐私。