Game theoretical study on client-controlled cloud data deduplication

Xueqin Liang, Zheng Yan*, Robert H. Deng

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Data deduplication not only requires a consummate technological designing, but also involves multiple parties with conflict interests. Thus, how to design incentive mechanisms and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we detail the payoff structure of a client-controlled deduplication scheme and analyze the feasibilities of unified discount and individualized discount under this structure. Through game theoretical study, a privacy-preserving individualized discount-based incentive mechanism is further proposed with detailed implementation algorithms for choosing strategies, setting parameters and granting discounts. After theoretical analysis on the requirements of individual rationality, incentive compatibility, and profitability, we conduct extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed incentive mechanism.

Original languageEnglish
Article number101730
Number of pages14
JournalComputers and Security
Publication statusPublished - 1 Apr 2020
MoE publication typeA1 Journal article-refereed


  • Cloud data deduplication
  • Free riding
  • Game theory
  • Incentive mechanism
  • Privacy


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