No abstract

A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things (IoT) era, which raises serious privacy concerns for millions of users. However, existing perturbation methods are not effective because of increased disclosure risk and reduced data utility, especially for small data sets. To overcome this issue, we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection. The edge server is used to mask users' sensitive data, which can not only avoid the data leakage caused by centralized perturbation, but also achieve better data utility than local perturbation. In addition, we present a global noise generation algorithm based on edge perturbation. Each edge server utilizes the global noise generated by the center server to perturb users' sensitive data. It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data. Finally, theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets.

Summary Existing dummy‐based trajectory privacy protection schemes do not take into account the correlation of multiple locations and whether the generated trajectory based on dummies matches the user's movement mode, which enables the adversary to identify some dummies. Aiming at this problem, to ensure that the generated trajectories match the movement modes of users, historical query trajectories of users are selected. In this way, the generated dummies on the selected historical query trajectories are based on the location relationship of adjacent time and the background information constraint of the dummy, namely, they should meet the time reachability, the similarity of historical query probability and the maximum in‐degree. Security analysis shows that the proposed scheme effectively perturbs the spatiotemporal correlation between the real location and dummies. Furthermore, the proposed scheme is compared with the existing schemes in terms of single‐point location exposure risk and trajectory exposure risk, and the experimental results indicate that the proposal has significant improvement in location privacy protection of the user.