Paper Info
Title
Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing.
Abstract
As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices' privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.
Year
DOI
Venue
2018
10.3390/s18082659
SENSORS
Keywords
Field
DocType
fog computing,Internet of Things,homomorphic encryption,privacy,data aggregation
Injection attacks,Computer security,Fog computing,Electronic engineering,Engineering,Data aggregator
Journal
Volume
Issue
Citations 
18
8.0
0
PageRank 
References 
Authors
0.34
37
7
Name
Order
Citations
PageRank
Yinghui Zhang146828.80
Jiangfan Zhao270.80
Dong Zheng3132.88
Kaixin Deng400.34
Fangyuan Ren521.38
Xiaokun Zheng632.40
Jiangang Shu790.78