Privacy-Preserving and Updatable Block-Level Data Deduplication in Cloud Storage Services

Hyungjune Shin, Dongyoung Koo, Youngjoo Shin, Junbeom Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


To achieve high storage saving, data deduplication techniques are widely used in many practical cloud storage services, which removes redundant data and keeps only a single copy of them. However, secure data deduplication over encrypted data is challenging since encryption may result in different ciphertexts even when the original messages are the same. Thus, message-locked encryption (MLE) is proposed to solve this issue and demonstrates that it is secure under the unpredictable message set. Since block-level deduplication can achieve more fine-grained storage saving, several block-level deduplication schemes that support updatability are also vividly proposed. However, the previous updatable block-level MLE schemes are vulnerable against brute-force attack when the message set is predictable. Since the size of a block is typically much less than an arbitrary size of a file, the predictability problem is a very important pragmatic concern which should be addressed in the block-level deduplication literature. In this paper, thus, we propose a novel secure block-level deduplication scheme that guarantees efficient data update and brute-force attack resilience even when messages are predictable with the rigorous security proof. Also, our performance evaluation shows that additional time and bandwidth usage can be minimized as the size of a block increases.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services
PublisherIEEE Computer Society
Number of pages9
ISBN (Electronic)9781538672358
Publication statusPublished - 2018 Sept 7
Event11th IEEE International Conference on Cloud Computing, CLOUD 2018 - San Francisco, United States
Duration: 2018 Jul 22018 Jul 7

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190


Other11th IEEE International Conference on Cloud Computing, CLOUD 2018
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2018-0-00269, A research on safe and convenient big data processing methods)

Publisher Copyright:
© 2018 IEEE.

Copyright 2019 Elsevier B.V., All rights reserved.


  • Brute-force attack
  • Cloud security
  • Secure deduplication
  • Storage management

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software


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