Game theoretical study on client-controlled cloud data deduplication

Abstract

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.