随着参与式感知PS技术的兴起，个人设备参与数据采集的规模和多样性不断增加，涌现了大量的多维数值型敏感数据，使隐私泄露风险变得更加严峻。为了解决这一问题，提出了一种参与式感知设备多维数值型数据的个性化差分隐私保护方案。该方案通过设计在一定范围内的个性化隐私预算分配方案，并优化DPM机制的采样维数，实现了最小化平均方差。在此基础上，设计了一种个性化的多维分段机制PDPM，提高了数据的可用性并使扰动后的均方误差更小。最后，在2个真实数据集上进行了实验，验证了所提方案在保护用户隐私的同时，显著降低了数值型数据的均方误差。因此，所提的方案在隐私保护和数据可用性之间提供了更好的平衡。

With the rise of Participatory Sensing technology, the scale and diversity of personal devices participating in data collection have continued to increase, leading to the emergence of a vast amount of multi dimensional numerical sensitive data, which has exacerbated the risk of privacy leakage. To address this issue, a personalized differential privacy protection scheme for multi dimensional numerical data from participatory sensing devices is proposed. This scheme achieves minimization of the mean squared error by designing a personalized privacy budget allocation scheme within a certain range and optimizing the sampling dimension of DPM (differential privacy mechanism). Based on this, PDPM (personalized dimensional partition mechanism) is designed to improve data availability and reduce the mean squared error after perturbation. Finally, experiments conducted on two real-world datasets verify that the proposed method significantly reduces the mean squared error of numerical data while protecting user privacy. Therefore, the proposed scheme provides a better balance between privacy protection and data availability.


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