Abstract: The existing localized differential privacy location protection methods mainly focus on protecting the static locations of users without considering the dynamic scenarios of location updating. Additionally, these methods often suffer from high algorithm complexity and low availability of perturbation results. In order to solve the above problems, a local differential privacy dynamic location protection method based on hidden Markov model (HMM) was proposed in this paper. Firstly, a time-series model and a privacy-preserving safe region based on the hidden Markov model and the privacy protection safety area were constructed by incorporating the spatial-temporal correlations of the dynamic changes of users’ locations, so as to optimize the local differential privacy perturbation regions after users’ location updates. Then, a hidden Markov model-based continuous perturbation algorithm and a random response mechanism of local differential privacy were designed to perturb the location points within the optimized region, realizing dynamically local differential privacy protection of users’ locations. Finally, experiments and analyses were carried out on the actual location trajectory dataset. The results demonstrate that the proposed method can achieve better aggregation accuracy and statistical availability on the premise of realizing dynamic local differential privacy protection of locations.

Key words: location privacy, local differential privacy, spatial-temporal correlation, hidden Markov model, privacy protection safety area