Privacy-preserving data aggregation against false data injection attacks in fog computing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yinghui Zhang
  • Jiangfan Zhao
  • Dong Zheng
  • Kaixin Deng
  • Fangyuan Ren
  • Xiaokun Zheng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number2659
Journal / PublicationSensors (Switzerland)
Volume18
Issue number8
Online published13 Aug 2018
Publication statusPublished - Aug 2018

Link(s)

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.

Research Area(s)

  • Data aggregation, Fog computing, Homomorphic encryption, Internet of things, Privacy

Citation Format(s)

Privacy-preserving data aggregation against false data injection attacks in fog computing. / Zhang, Yinghui; Zhao, Jiangfan; Zheng, Dong et al.

In: Sensors (Switzerland), Vol. 18, No. 8, 2659, 08.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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