Location Privacy Protection Method Based on Local Differential Privacy in Crowdsensing With Approximately Accurate Task Allocation

With the widespread adoption of smartphones and other mobile intelligent devices, Mobile Crowd Sensing (MCS) is widely used. Typically, the real locations of the workers and tasks must be submitted to the service platform to complete the task allocation. Therefore, the protection of location information has become a key factor in influencing user participation. To address the issue of location information leakage, we propose a location information protection method based on local differential privacy, which can protect the location privacy of workers and tasks while generating approximately accurate task allocation results. Firstly, we divide the region into $k$*$k$ grids and merge girds with a similar dispersion to form clusters. Then, this paper utilizes the inverse sampling of the cumulative distribution function (CDF) of the flipped Huber distribution to generate a personalized noise location set for each cluster. Furthermore, the exponential mechanism is used to select the obfuscated location for each user. Finally, the platform selects workers based on the perturbed location to complete the task allocation. Theoretical analysis shows that our mechanism satisfies differential privacy and achieves an approximately accurate task allocation. Experimental results demonstrate that, compared to existing methods, this method exhibits superior performance across different datasets and effectively balances the utility of data and the protection of location privacy.