Blockchain Privacy Disclosure Risk Assessment Scheme Based on Improved Paillier Algorithm

Abstract

Statistical analysis of medical and health data is an important task. However, due to privacy limitations, existing privacy protection systems cannot balance data availability and privacy, that is, ensure data availability while preventing patient privacy leakage. As a distributed ledger shared among nodes of a computer network, blockchain unique feature can provide a powerful security and privacy protection for user data, and homomorphic encryption can be directly to manipulate the characteristics of the ciphertext to ensure the availability of data and privacy, combining both this study proposes a blockchain node classification scheme based on homomorphic encryption. To evaluate the data privacy security on the blockchain, specifically, we store transaction data according to the node classification through the improved Paillier algorithm and node classification, evaluate the risks of the blockchain, and compare the data privacy and security before and after homomorphic encryption. Finally, the feasibility and effectiveness of the algorithm are verified by simulation experiments.