Article
Node Location Privacy Protection Based on
Differentially Private Grids in Industrial Wireless
Sensor Networks
Jun Wang
1
ID
, Rongbo Zhu
1,
*, Shubo Liu
2
and Zhaohui Cai
2
1
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China;
jameswang@whu.edu.cn
2
School of Computer, Wuhan University, Wuhan 430074, China; lsb_whu@126.com (S.L.);
zhcai@whu.edu.cn (Z.C.)
* Correspondence: rbzhu@mail.scuec.edu.cn
Received: 31 December 2017; Accepted: 25 January 2018; Published: 31 January 2018
Abstract:
Wireless sensor networks (WSNs) are widely applied in industrial application with the
rapid development of Industry 4.0. Combining with centralized cloud platform, the enormous
computational power is provided for data analysis, such as strategy control and policy making.
However, the data analysis and mining will bring the issue of privacy leakage since sensors will
collect varieties of data including sensitive location information of monitored objects. Differential
privacy is a novel technique that can prevent compromising single record benefits. Geospatial data
can be indexed by a tree structure; however, existing differentially private release methods pay no
attention to the concrete analysis about the partition granularity of data domains. Based on the overall
analysis of noise error and non-uniformity error, this paper proposes a data domain partitioning
model, which is more accurate to choose the grid size. A uniform grid release method is put forward
based on this model. In order to further reduce the errors, similar cells are merged, and then noise is
added into the merged cells. Results show that our method significantly improves the query accuracy
compared with other existing methods.
Keywords: location; privacy guarantee; differential privacy; industrial wireless sensor networks
1. Introduction
Recent years have witnessed the rapid development of the industrial wireless sensor networks
(IWSNs), which have been introduced into the industry area to meet requirements of higher flexibility
and market share, and IWSNs are becoming the key and fundamental technology of Industry 4.0 [
1
].
In the industrial domain, mobile nodes are used in industrial systems incrementally [
2
]. Radio modules
or wireless nodes have been installed on mobile devices to raise mobility and flexibility which are
ignored in traditional WSNs [
3
]. IWSNs generally contain more moving nodes, such as mobile products,
workers and other mobile devices [
4
]. The centralized cloud platform collects sensor data to provide
strategy control and policy making. However, the data analysis and mining will bring the issue of
privacy leakage since a semi-credible cloud server is curious about sensitive location information of
monitored objects [
5
]. To address this problem, a novel privacy protection technique called differential
privacy [6] has been introduced to location privacy preservation.
The Location information is called geospatial data [
7
]. For example, as shown in Figure 1,
the location information of nodes will be collected and uploaded to the cloud. The release of static
geospatial data brings great convenience to scientific research. The work in [
8
] indicates that it is possible
to use geospatial information to forecast the spread of an infectious disease. However, data analysis
also has the risk of privacy leaks. For instance, De Montjoye demonstrated that only simple date and
Sensors 2018, 18, 410; doi:10.3390/s18020410 www.mdpi.com/journal/sensors
