Secure Data Assimilation of Cloud Sensor Networks

Quanyan Zhu; Zhiheng Xu

doi:10.1007/978-3-030-60251-2_5

Secure Data Assimilation of Cloud Sensor Networks

Quanyan Zhu, Zhiheng Xu

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Cloud computing technologies (CCTs) enable a large-scale sensor network (LSN) to outsource the computations of data assimilation to improve its performance. However, the cyber-physical nature of cloud-enabled LSNs (CE-LSNs) introduces new challenges. Outsourcing the computations to an untrusted cloud may expose the privacy of the sensing data. To address the security issues, this chapter proposes a security mechanism to achieve data confidentiality in the outsourcing process. We develop our mechanism by combining a conventional homomorphic encryption and a customized encryption scheme. We present theorems to characterize the correctness of the encryption and investigate the estimation performance and the security of the proposed method. We also analyze the impacts of the quantization errors on the estimation performance. Finally, we present numerical experiments to consolidate our analytical results.

Original language	English (US)
Title of host publication	Advances in Information Security
Publisher	Springer
Pages	43-58
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-030-60251-2_5
State	Published - 2020

Publication series

Name	Advances in Information Security
Volume	81
ISSN (Print)	1568-2633

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications

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10.1007/978-3-030-60251-2_5

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abstract = "Cloud computing technologies (CCTs) enable a large-scale sensor network (LSN) to outsource the computations of data assimilation to improve its performance. However, the cyber-physical nature of cloud-enabled LSNs (CE-LSNs) introduces new challenges. Outsourcing the computations to an untrusted cloud may expose the privacy of the sensing data. To address the security issues, this chapter proposes a security mechanism to achieve data confidentiality in the outsourcing process. We develop our mechanism by combining a conventional homomorphic encryption and a customized encryption scheme. We present theorems to characterize the correctness of the encryption and investigate the estimation performance and the security of the proposed method. We also analyze the impacts of the quantization errors on the estimation performance. Finally, we present numerical experiments to consolidate our analytical results.",

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AB - Cloud computing technologies (CCTs) enable a large-scale sensor network (LSN) to outsource the computations of data assimilation to improve its performance. However, the cyber-physical nature of cloud-enabled LSNs (CE-LSNs) introduces new challenges. Outsourcing the computations to an untrusted cloud may expose the privacy of the sensing data. To address the security issues, this chapter proposes a security mechanism to achieve data confidentiality in the outsourcing process. We develop our mechanism by combining a conventional homomorphic encryption and a customized encryption scheme. We present theorems to characterize the correctness of the encryption and investigate the estimation performance and the security of the proposed method. We also analyze the impacts of the quantization errors on the estimation performance. Finally, we present numerical experiments to consolidate our analytical results.

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Secure Data Assimilation of Cloud Sensor Networks

Abstract

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