World Wide Web

, Volume 21, Issue 4, pp 915–938 | Cite as

An effective approach for the protection of privacy text data in the CloudDB

  • Zongda Wu
  • Guandong Xu
  • Chenglang Lu
  • Enhong Chen
  • Fang Jiang
  • Guiling LiEmail author


Due to the advantages of pay-on-demand, expand-on-demand and high availability, cloud databases (CloudDB) have been widely used in information systems. However, since a CloudDB is distributed on an untrusted cloud side, it is an important problem how to effectively protect massive private information in the CloudDB. Although traditional security strategies (such as identity authentication and access control) can prevent illegal users from accessing unauthorized data, they cannot prevent internal users at the cloud side from accessing and exposing personal privacy information. In this paper, we propose a client-based approach to protect personal privacy in a CloudDB. In the approach, privacy data before being stored into the cloud side, would be encrypted using a traditional encryption algorithm, so as to ensure the security of privacy data. To execute various kinds of query operations over the encrypted data efficiently, the encrypted data would be also augmented with additional feature index, so that as much of each query operation as possible can be processed on the cloud side without the need to decrypt the data. To this end, we explore how the feature index of privacy data is constructed, and how a query operation over privacy data is transformed into a new query operation over the index data so that it can be executed on the cloud side correctly. The effectiveness of the approach is demonstrated by theoretical analysis and experimental evaluation. The results show that the approach has good performance in terms of security, usability and efficiency, thus effective to protect personal privacy in the CloudDB.


CloudDB Information system Privacy protection Feature index 



We would like to thank anonymous reviewers for their valuable comments. The work is supported by the National Social Science Fund of China (No. 17CTQ011).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Zongda Wu
    • 1
    • 2
  • Guandong Xu
    • 3
  • Chenglang Lu
    • 1
  • Enhong Chen
    • 2
  • Fang Jiang
    • 1
  • Guiling Li
    • 4
    Email author
  1. 1.Oujiang CollegeWenzhou UniversityWenzhouChina
  2. 2.School of Computer ScienceUniversity of Science and Technology of ChinaHefeiChina
  3. 3.Faculty of Engineering and ITUniversity of TechnologySydneyAustralia
  4. 4.School of Computer ScienceChina University of GeosciencesWuhanChina

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