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Secure Data Storage and Retrieval in Cloud Computing

Research Area:  Cloud Computing

Abstract:

  Nowadays cloud computing has been widely recognized as one of the most influential information technologies because of its unprecedented advantages. In spite of its widely recognized social and economic benefits, in cloud computing customers lose the direct control of their data and completely rely on the cloud to manage their data and computation, which raises significant security and privacy concerns and is one of the major barriers to the adoption of public cloud by many organizations and individuals. Therefore, it is desirable to apply practical security approaches to address the security risks for the wide adoption of cloud computing.
  In this thesis, we carry out the study on the secure data storage and retrieval in cloud computing. Data storage outsourcing is one of the important cloud applications where both individuals and enterprises can store their data remotely on the cloud to relieve the storage management burden. Aside from eliminating the local storage management, storing data into the cloud requires that the data can be efficiently and securely retrieved for flexible utilization. To provide strong security guarantees for data storage and retrieval, in this thesis, we have made the following contributions.
  Firstly, we give a formal treatment on Merkle Hash Tree for secure dynamic cloud auditing. We first revisit a well-known authentication structure named Merkle Hash Tree (MHT) and demonstrate how to extend its basic version to a sequence-enforced version that allows position checking. In order to support efficient and verifiable dynamic data operations, we further propose a variant of MHT, named rank-based MHT (rMHT) that can be used to support verifiable dynamic data auditing. We also review a data auditing protocol named Oruta and showed that O ruta is vulnerable to replace and replay attacks. We then employ the proposed rMHT to fix the security problems in O ruta without sacrificing any desirable features of the protocol

Name of the Researcher:  Rongmao Chen

Name of the Supervisor(s):  Yi Mu, Guomin Yang

Year of Completion:  2016

University:  The University of Wollongong

Thesis Link:   Home Page Url