Affiliation:
1. School of Computer Science, Qufu Normal University, Rizhao 276826, China
2. School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Abstract
Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. As an important part of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in military, transportation, medical, and household fields. However, in the applications of wireless sensor networks, the adversary can infer the location of a source node and an event by backtracking attacks and traffic analysis. The location privacy leakage of a source node has become one of the most urgent problems to be solved in wireless sensor networks. To solve the problem of source location privacy leakage, in this paper, we first propose a proxy source node selection mechanism by constructing the candidate region. Secondly, based on the residual energy of the node, we propose a shortest routing algorithm to achieve better forwarding efficiency. Finally, by combining the proposed proxy source node selection mechanism with the proposed shortest routing algorithm based on the residual energy, we further propose a new, anonymous communication scheme. Meanwhile, the performance analysis indicates that the anonymous communication scheme can effectively protect the location privacy of the source nodes and reduce the network overhead.
Funder
EU Horizon 2020 DOMINOES Project
Subject
Computer Networks and Communications,Information Systems
Reference37 articles.
1. Towards the science of security and privacy in machine learning;N. Papernot,2016
2. Belief and fairness: a secure two-party protocol toward the view of entropy for IoT devices;Y. Wang;Journal of Network and Computer Applications,2020
3. Research on source location privacy protection based on phantom routing;M. Lu;Information Security and Technology,2012
4. A Source-Location Privacy Preservation Protocol in Wireless Sensor Networks Using Source-Based Restricted Flooding
5. SecureML: a system for scalable privacy-preserving machine learning;P. Mohassel
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