Flexible Local Differential Privacy Mechanism for Collecting Location Data

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To address issues in existing location data collection methods, such as poor data utility and large deviations between perturbed and true locations, this paper proposes a personalized local differential privacy (LDP) mechanism for location data collection. Users can select their privacy protection range based on their needs, limiting the perturbed output to this range, thereby improving data utility. To address the issue of large deviations between perturbed and true locations, we introduce a new strategy where the location is perturbed with a probability that is higher the closer it is to the true location. By analyzing the mutual information upper bound of the true and estimated location distributions, the optimal perturbation probability range is determined. Finally, a probability transition matrix is generated from the location transfer probabilities, and the true location distribution is estimated from the perturbed location distribution.