Title: Outsourced data modification algorithm with assistance of multi-assistants in cloud computing

Authors: Jian Shen; Jun Shen; Xiong Li; Fushan Wei; Jiguo Li

Addresses: Department of Jiangsu Engineering Center of Network Monitoring, Jiangsu, 210044, China; Technology and Engineering Center of Meteorological Sensor Network, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing, Jiangsu, 210044, China; School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China ' Hunan University of Science and Technology, Hunan, 410082, China ' The PLA Information Engineering University, Henan, 450002, China ' Hohai University, Nanjing, 210098, China

Abstract: The rapid development of cloud storage has caused a wave of research craze. To improve cloud user experience, a large number of schemes are proposed with various practical performances, like long-term correct ensurence and dynamic support. In most works, however, authors seem to completely ignore that data owner alone could not have enough energy to discover and correct all inappropriate data outsourced. Others considered it and gave more than one user both read and write permissions, leading to chaotic management of multiusers. In this paper, we propose a novel algorithm, where data owner and authenticated assistants form a team to support data dynamics. Assistants are in charge of detecting problems of data and discussing a corresponding modification suggestion, while data owner is responsible for the implementation of the modification. Additionally, our algorithm supports identity authentication, malicious assistant revocation, and lazy update. Sufficient numerical analysis validates the performance of our algorithm.

Keywords: cloud storage; assistants; data modification; identity authentication; assistant revocation; lazy update.

DOI: 10.1504/IJSNET.2017.084230

International Journal of Sensor Networks, 2017 Vol.24 No.1, pp.62 - 73

Received: 24 Aug 2016
Accepted: 13 Oct 2016

Published online: 21 May 2017 *

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