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Secure and efficient big data deduplication in fog computing

  • Jiajun Yan
  • Xiaoming WangEmail author
  • Qingqing Gan
  • Suyu Li
  • Daxin Huang
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Abstract

With the rapid development of the Internet of Things, the massive amount of big data generated by the Internet of Things terminals and the real-time processing requirements have brought enormous challenges. A two-tier computing model consisting solely of two entities, cloud and user, will not be sufficient to support processing large numbers of concurrent data requests. Therefore, fog computing was proposed. How to realize the secure and efficient deduplication of ciphertext in fog computing has become a new research topic. In this paper, we firstly present a new decentralized deduplication structure and then show how to apply it to construct a secure and efficient big data deduplication scheme in fog computing. The cloud server, in the proposed paper, can quickly determine which fog server needs to be traversed to search duplicate data, and instead of traversing all fog servers. This significantly improves the efficiency of big data deduplication in fog computing. Furthermore, the proposed scheme allows fog server to verify whether the user possesses the ownership of the data. Performance analysis and experimental results show the proposed scheme has less overheads than existing schemes.

Keywords

Fog computing Secure deduplication Proof of ownership Efficiency 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61070164 and Grant 61272415, in part by the Natural Science Foundation of Guangdong Provience, China, under Grant S2012010008767, in part by the Science and Technology Planning Project of Guangdong Provience, China, under Grant 2013B010401015, and in part by the Zhuhai Top Discipline-Information Security.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jiajun Yan
    • 1
  • Xiaoming Wang
    • 1
    Email author
  • Qingqing Gan
    • 1
  • Suyu Li
    • 1
  • Daxin Huang
    • 1
  1. 1.The Department of Computer ScienceJinan UniversityGuangzhouChina

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