Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain - Xu, Ze; Cao, Sanxing (Computer modeling in engineering & sciences, 2023)
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 protection methods have been proposed to solve the problem of privacy leakage during the process of data sharing, but they suffer from two 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 of multi-source heterogeneous data in the dissemination of media and reduces ciphertext processing time. We deployed the proposed method on the Hyperledger platform for testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy. The experimental results show that the key generation, encryption, and decryption times of the proposed method are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology. This significantly reduces the processing time of multi-source data, which gives it potential for use in many applications.