Sparse Mobile Crowdsensing (MCS) has become a compelling approach to acquire and infer urban-scale sensing data. However, participants risk their location privacy when reporting data with their actual sensing positions. To address this issue, we propose a novel location obfuscation mechanism combining E-differential-privacy and δ-distortion-privacy in Sparse MCS. More specifically, differential privacy bounds adversaries' relative information gain regardless of their prior knowledge, while distortion privacy ensures that the expected inference error is larger than a threshold under an assumption of adversaries' prior knowledge. To reduce the data quality loss incurred by location obfuscation, we design a differential-and-distortion privacy-preserving framework with three components. First, we learn a data adjustment function to fit the original sensing data to the obfuscated location. Second, we apply a linear program to select an optimal location obfuscation function. The linear program aims to minimize the uncertainty in data adjustment under the constraints of E-differential-privacy, δ-distortion-privacy, and evenly-distributed obfuscation. We also design an approximated method to reduce the required computation resources. Third, we propose an uncertainty-aware inference algorithm to improve the inference accuracy for the obfuscated data. Evaluations with real environment and traffic datasets show that our optimal method reduces the data quality loss by up to 42% compared to the state-of-the-art methods with the same level of privacy protection; the approximated method incurs <; 3% additional quality loss than the optimal method, but only needs <; 1% of the computation time.

中文翻译：

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具有差分和失真位置隐私的稀疏移动人群

稀疏移动人群感知（MCS）已成为获取和推断城市规模感测数据的一种引人注目的方法。但是，参与者在报告带有实际感应位置的数据时会冒着位置隐私的风险。为了解决这个问题，我们提出了一种在稀疏MCS中结合E-差分隐私和δ-失真隐私的新型位置混淆机制。更具体地，差异性隐私限制了对手的相对信息增益，而不管其先验知识如何，而失真隐私确保了在假定对手的先验知识的情况下预期的推理误差大于阈值。为了减少位置混淆带来的数据质量损失，我们设计了一个包含三个部分的差分和失真隐私保护框架。第一，我们学习了一种数据调整功能，以将原始感测数据拟合到模糊的位置。其次，我们应用线性程序来选择最佳位置模糊处理函数。线性程序的目的是在E-差分隐私，δ-失真-隐私和均匀分布的混淆的约束下将数据调整的不确定性最小化。我们还设计了一种近似方法来减少所需的计算资源。第三，我们提出了一种不确定性感知推理算法，以提高对模糊数据的推理精度。对真实环境和流量数据集的评估表明，与具有相同隐私保护级别的最新方法相比，我们的最佳方法可将数据质量损失降低多达42％；近似方法导致<; 比最佳方法增加3％的质量损失，但仅需<; 1％的计算时间。