A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT

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.

Automated summary

An increasing amount of data containing users' sensitive information is being collected by smart connected devices in the IoT era, posing privacy concerns. Existing perturbation methods are ineffective for small data sets. To address this issue, we propose a new edge perturbation mechanism based on global sensitivity concept to protect sensitive information in IoT data collection. By utilizing edge servers and a global noise generation algorithm, the proposed mechanism achieves better data utility and minimizes disclosure risk.