基于用户相关性的差分隐私轨迹隐私保护方案.

When using location-based services, users need to provide their real location information, which may lead to the leakage of user information. Most research only focuses on the privacy protection of individual users, while ignoring the correlation among multiple users. This paper proposed a differential privacy trajectory protection scheme based on user correlation for trajectory privacy protection issues involving multiple users. Firstly, it constructed a historical trajectory tree and used a variable-order Markov model to predict user trajectories, generating a set of highly usable trajectory datasets from the collection of trajectories. Secondly, it obtained a set of predicted trajectories with lower correlation based on the inter-user trajectory correlations. Finally, by customizing the privacy budget method, it dynamically adjusted the privacy budget for each location point according to different user privacy needs and added Laplacian noise to the published trajectories. Experimental results show that compared to the LPADP algorithm, this algorithm improves execution efficiency by 10%~15.9%. Compared to both PTPP and LPADP algorithms, it enhances data usability by 11%~16.1%, while also increasing the level of privacy protection. [ABSTRACT FROM AUTHOR]