Abstract: 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.

Key words: participatory sensing, local differential privacy, personalized segmentation mechanism, multidimensional numerical data, privacy protection