A differential privacy location protect approach with intelligence data collection paradigm for MCS

Location information is often required for task allocation in mobile crowdsensing. However, directly uploading location information can raise concerns about privacy leakage among users. To address this issue and protect users privacy when uploading location data to the server, this paper proposes a novel method based on differential privacy for preserving location information. The main idea is to utilize Hilbert mapping to transform the two-dimensional location coordinate of the user into a one-dimensional Hilbert index. Then, Laplace noise is added to the index to introduce a perturbed index, ensuring the protection of location privacy. To enhance the usability of the perturbed index, this paper treats the Laplace noise as a random variable and employs mathematical integration to measure the distance between users and tasks. Leveraging the characteristic of the Hilbert index, wherein a small change in the indexes can result in a large distance between corresponding coordinates, the combination of differential privacy and Hilbert mapping offers enhanced effectiveness compared to conventional differential privacy location preservation schemes. Finally, the proposed scheme is evaluated using the real dataset, and the experiments validate its efficacy.