Soft Computing

, Volume 21, Issue 8, pp 2175–2187

Enabling public auditability for operation behaviors in cloud storage

  • Hui Tian
  • Zhaoyi Chen
  • Chin-Chen Chang
  • Minoru Kuribayashi
  • Yongfeng Huang
  • Yiqiao Cai
  • Yonghong Chen
  • Tian Wang
Methodologies and Application

DOI: 10.1007/s00500-016-2311-y

Cite this article as:
Tian, H., Chen, Z., Chang, CC. et al. Soft Comput (2017) 21: 2175. doi:10.1007/s00500-016-2311-y
  • 227 Downloads

Abstract

In this paper, we focus on auditing for users’ operation behaviors, which is significant for the avoidance of potential crimes in the cloud and equitable accountability determination in the forensic. We first present a public model for operation behaviors in cloud storage, in which a trusted third party is introduced to verify the integrity of operation behavior logs to enhance the credibility of forensic results as well as alleviate the burden of the forensic investigator. Further, we design a block-based logging approach to support selective verification and a hash-chain-based structure for each log block to ensure the forward security and append-only properties for log entries. Moreover, to achieve the tamper resistance of log blocks and non-repudiation of auditing proofs, we employ Merkle hash tree (MHT) to record the hash values of the aggregation authentication block tags sequentially and publish the root of MHT to the public once a block has been appended. Meanwhile, using the authentication property of MHT, our scheme can provide log-less verification with privacy preservation. We formally prove the security of the proposed scheme and evaluate its performance on entry appending and verification by concrete experiments and comparisons with the state-of-the-art schemes. The results demonstrate that the proposed scheme can effectively achieve secure auditing for log files of operation behaviors in cloud storage and outperforms the previous ones in computation complexity and communication overhead.

Keywords

Cloud storage Public auditing Operation behaviors Merkle hash tree Secure logging 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Hui Tian
    • 1
  • Zhaoyi Chen
    • 1
  • Chin-Chen Chang
    • 2
  • Minoru Kuribayashi
    • 3
  • Yongfeng Huang
    • 4
  • Yiqiao Cai
    • 1
  • Yonghong Chen
    • 1
  • Tian Wang
    • 1
  1. 1.College of Computer Science and TechnologyNational Huaqiao UniversityXiamenChina
  2. 2.Department of Information Engineering and Computer ScienceFeng Chia UniversityTaichungTaiwan
  3. 3.Graduate School of Natural Science and TechnologyOkayama UniversityOkayamaJapan
  4. 4.Department of Electronic EngineeringTsinghua UniversityBeijingChina

Personalised recommendations