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Neural Computing and Applications

Erschienen in:

21.06.2020 | S.I. : DPTA Conference 2019

Improved algorithm for management of outsourced database

verfasst von: Zhen Guo, Jun Ye

Erschienen in: Neural Computing and Applications | Ausgabe 2/2021

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Abstract

In cloud storage, clients outsource data storage to save local resources. Clients lose the controllability to manage data, and cloud service providers are usually untrusted. When clients want to know the correctness of data anytime, they have to verify the data. In this paper, we focus on the validation of outsourced data. We propose a data correctness verification scheme to verify the correctness of cloud storage data. By calculating the validation data, when the user queries the data, it is easy to determine whether the cloud server returns the correct data. In addition, clients can update outsourced data. When updating the database, the clients can verify the correctness of the updated file by calculating and producing a new proof. The efficiency of data validation is very high, and the computational overhead on the client side is very low. This scheme is suitable for the resource-constrained devices, such as wearable devices. This scheme can be applied in the IoT perception layer with limited computing resources.

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Titel: Improved algorithm for management of outsourced database
verfasst von: Zhen Guo
Jun Ye
Publikationsdatum: 21.06.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 2/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05047-7

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