A Secure Data Assimilation for Large-Scale Sensor Networks Using an Untrusted Cloud

被引：0
|
作者：
Xu, Zhiheng [1 ]
Zhu, Quanyan [1 ]
机构：
[1] NYU, Tandon Sch Engn, Brooklyn, NY 11201 USA
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
基金：
美国国家科学基金会;
关键词：
Homomorphic Encryption; Cloud Computing; Sensor Networks; Data Assimilation;
D O I：
10.1016/j.ifacol.2017.08.135
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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, we proposed a secure approach 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. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2609 / 2614
页数：6
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