Research on Mass Data Retrieval Model Method Based on Privacy Computing

Abstract

In order to protect data privacy in the context of big data, a new scheme to protect data privacy and user privacy is proposed. This study believes that blockchain technology can be used to build a massive private data retrieval and sharing platform, considering the realization of privacy protection under horizontal federated learning and vertical federated learning, combined with privacy computing technology, to verify the results of massive data retrieval, and to build a "data availability that is not available." The "visible" security reduction model involves three cryptographic algorithms, namely functional encryption, zero-knowledge proof and asymmetric encryption. By combining blockchain and cloud servers, a hybrid storage architecture with data storage on the chain and off-chain storage is realized. And use function encryption and zero-knowledge proof to achieve privacy protection and secure data sharing of verifiable results. The final experimental results prove that the security feasibility of this model is proposed in this paper, which can fully improve the level of data log security management, operation and maintenance supervision, and the quality and efficiency of data security protection, and improve the network security protection system of the power grid in all scenarios.