Abstract
Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user’s original data, and fundamentally protects the user’s data privacy. During this process, after receiving the data of the user’s local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility.
Highlights
The boom in equipment manufacturing, communication technology, data processing, algorithms, together with the emergence of Internet of Things (IoT), gives rise to the Crowd-Sensing [1,2], a key access to the formation of information value service from the physical world
In order to overcome the difficulties in compatibility between local privacy and high-dimension data in existing privacy protection mechanisms of Crowd-Sensing system, we propose a local privacy protection mechanism for high-dimensional perceptual data based on Bayes network, and the main contributions are as follows
This paper mainly focuses on local differential privacy protection during the publication of high-dimensional perception data in the Crowd-Sensing system
Summary
The boom in equipment manufacturing, communication technology, data processing, algorithms, together with the emergence of Internet of Things (IoT), gives rise to the Crowd-Sensing [1,2], a key access to the formation of information value service from the physical world. Various smart devices, which are portable in large space, can realize the perception and digitization of the physical world across time and space. In addition to its large scale, Crowd-Sensing data obtained by immense heterogeneous sensing devices boast the attributes of multidimension or even high-dimensional characteristics in many cases, and, mining the correlation among. Sensors 2020, 20, 2516 multidimension or even high-dimensional characteristics in many cases, and, mining the correlation among the attribute dimensions is vital to the value of Crowd-Sensing data. Attribute dimensions vital to features the valueinofa Crowd-Sensing example, the correlation the correlation analysis ofisphysical patient’s health data
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