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Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks

The privacy and security of the Internet of Things (IoT) are emerging as popular issues in the IoT. At present, there exist several pieces of research on network analysis on the IoT network, and malicious network analysis may threaten the privacy and security of the leader in the IoT networks. With...

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Autores principales: Ji, Jie, Wu, Guohua, Shuai, Jinguo, Zhang, Zhen, Wang, Zhen, Ren, Yizhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767009/
https://www.ncbi.nlm.nih.gov/pubmed/31505866
http://dx.doi.org/10.3390/s19183886
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author Ji, Jie
Wu, Guohua
Shuai, Jinguo
Zhang, Zhen
Wang, Zhen
Ren, Yizhi
author_facet Ji, Jie
Wu, Guohua
Shuai, Jinguo
Zhang, Zhen
Wang, Zhen
Ren, Yizhi
author_sort Ji, Jie
collection PubMed
description The privacy and security of the Internet of Things (IoT) are emerging as popular issues in the IoT. At present, there exist several pieces of research on network analysis on the IoT network, and malicious network analysis may threaten the privacy and security of the leader in the IoT networks. With this in mind, we focus on how to avoid malicious network analysis by modifying the topology of the IoT network and we choose closeness centrality as the network analysis tool. This paper makes three key contributions toward this problem: (1) An optimization problem of removing k edges to minimize (maximize) the closeness value (rank) of the leader; (2) A greedy (greedy and simulated annealing) algorithm to solve the closeness value (rank) case of the proposed optimization problem in polynomial time; and (3)UpdateCloseness (FastTopRank)—algorithm for computing closeness value (rank) efficiently. Experimental results prove the efficiency of our pruning algorithms and show that our heuristic algorithms can obtain accurate solutions compared with the optimal solution (the approximation ratio in the worst case is 0.85) and outperform the solutions obtained by other baseline algorithms (e.g., choose k edges with the highest degree sum).
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spelling pubmed-67670092019-10-02 Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks Ji, Jie Wu, Guohua Shuai, Jinguo Zhang, Zhen Wang, Zhen Ren, Yizhi Sensors (Basel) Article The privacy and security of the Internet of Things (IoT) are emerging as popular issues in the IoT. At present, there exist several pieces of research on network analysis on the IoT network, and malicious network analysis may threaten the privacy and security of the leader in the IoT networks. With this in mind, we focus on how to avoid malicious network analysis by modifying the topology of the IoT network and we choose closeness centrality as the network analysis tool. This paper makes three key contributions toward this problem: (1) An optimization problem of removing k edges to minimize (maximize) the closeness value (rank) of the leader; (2) A greedy (greedy and simulated annealing) algorithm to solve the closeness value (rank) case of the proposed optimization problem in polynomial time; and (3)UpdateCloseness (FastTopRank)—algorithm for computing closeness value (rank) efficiently. Experimental results prove the efficiency of our pruning algorithms and show that our heuristic algorithms can obtain accurate solutions compared with the optimal solution (the approximation ratio in the worst case is 0.85) and outperform the solutions obtained by other baseline algorithms (e.g., choose k edges with the highest degree sum). MDPI 2019-09-09 /pmc/articles/PMC6767009/ /pubmed/31505866 http://dx.doi.org/10.3390/s19183886 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ji, Jie
Wu, Guohua
Shuai, Jinguo
Zhang, Zhen
Wang, Zhen
Ren, Yizhi
Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title_full Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title_fullStr Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title_full_unstemmed Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title_short Heuristic Approaches for Enhancing the Privacy of the Leader in IoT Networks
title_sort heuristic approaches for enhancing the privacy of the leader in iot networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767009/
https://www.ncbi.nlm.nih.gov/pubmed/31505866
http://dx.doi.org/10.3390/s19183886
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