Blockchain-Based Pseudonymization Method for Enhanced Data Privacy Management

Blockchain-Based Pseudonymization Method for Enhanced Data Privacy Management

In this paper, I study the application of blockchain technology in environments that require accurate handling of large-scale data, such as artificial intelligence, to enhance prediction accuracy and data performance. To address data privacy concerns and to strengthen trust in Data Privacy and security, I have researched the application-based performance of zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) using formulated approaches. For performance evaluation, I designed and developed a smart contract based on the proposed content to ensure the implementation of zk-SNARKs. The results indicate that when compared to traditional pseudonymization algorithms like Pseudonymization and tokenization, zk-SNARKs improve confidentiality by 5-10%, data privacy by over 10%, and security by more than 20%.