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作 者:Ze Xu Sanxing Cao
出 处:《Computer Modeling in Engineering & Sciences》2023年第7期861-881,共21页工程与科学中的计算机建模(英文)
基 金:funded by the High-Quality and Cutting-Edge Discipline Construction Project for Universities in Beijing (Internet Information,Communication University of China).
摘 要:Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.
关 键 词:Homomorphic encryption blockchain technology multi-source data data privacy protection privacy data processing
分 类 号:TP309[自动化与计算机技术—计算机系统结构]
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