Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for sta...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 6; no. 2; pp. 1530 - 1540
Main Authors: Xiong, Jinbo, Ren, Jun, Chen, Lei, Yao, Zhiqiang, Lin, Mingwei, Wu, Dapeng, Niu, Ben
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k -means algorithm
Algorithms
Availability
Clustering
Clustering algorithms
Data analysis
Data clustering
Data mining
Detection
differential privacy
Electricity
Electricity consumption
Electronic devices
Internet of Things
Internet of Things (IoT)
Outliers (statistics)
Performance evaluation
Privacy
privacy protection
Remote sensors
Security
Statistical analysis
Wireless sensor networks
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy <inline-formula> <tex-math notation="LaTeX"> {k} </tex-math></inline-formula>-means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum's algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on <inline-formula> <tex-math notation="LaTeX"> {k} </tex-math></inline-formula>-means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy <inline-formula> <tex-math notation="LaTeX"> {k} </tex-math></inline-formula>-means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2842773