A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT
TLDR
The proposed Lightweight Privacy-preserving data aggregation scheme, called LPDA, is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge.Abstract:
Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices’ data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.read more
Citations
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Journal ArticleDOI
A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures
TL;DR: A detailed review of the security-related challenges and sources of threat in the IoT applications is presented and four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.
Journal ArticleDOI
All one needs to know about fog computing and related edge computing paradigms: A complete survey
Ashkan Yousefpour,Caleb Fung,Tam T. Nguyen,Krishna P. Kadiyala,Fatemeh Jalali,Amirreza Niakanlahiji,Jian Kong,Jason P. Jue +7 more
TL;DR: This paper provides a tutorial on fog computing and its related computing paradigms, including their similarities and differences, and provides a taxonomy of research topics in fog computing.
Journal ArticleDOI
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions
TL;DR: The architecture and features of fog computing are reviewed and critical roles of fog nodes are studied, including real-time services, transient storage, data dissemination and decentralized computation, which are expected to draw more attention and efforts into this new architecture.
Journal ArticleDOI
Internet of things security: A top-down survey
TL;DR: A comprehensive top down survey of the most recent proposed security and privacy solutions in IoT in terms of flexibility and scalability and a general classification of existing solutions is given.
Journal ArticleDOI
Security and Privacy in Fog Computing: Challenges
Mithun Mukherjee,Rakesh Matam,Lei Shu,Leandros A. Maglaras,Mohamed Amine Ferrag,Nikumani Choudhury,Vikas Kumar +6 more
TL;DR: This paper provides an overview of existing security and privacy concerns, particularly for the fog computing, and highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.
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