Research on data security and privacy protection in machine learning scenarios leveraging blockchain technology

With the rapid advancement of artificial intelligence, machine learning has been increasingly applied across various domains. However, the issues of data security and privacy protection in machine learning have also emerged as prominent challenges. This study delves into these challenges and proposes a solution that integrates blockchain technology. The proposed solution encompasses three critical stages: data preprocessing, model training, and model inference. Specifically, it includes secure feature extraction during data preprocessing, distributed training strategies during model training, and secure data inference during model inference. By implementing measures such as data sharding and encrypted storage, on-chain feature extraction, encrypted upload of gradient information, validation through consensus mechanisms, and smart contract-driven model parameter updates, this study ensures data security and privacy throughout its lifecycle. The results confirm that the proposed solution not only enhances the efficiency and performance of machine learning models but also bolsters transparency and traceability in data processing, providing a novel perspective on data security and privacy protection. Furthermore, the findings of this study open new avenues for sustainable development in the field of artificial intelligence, suggesting future research will further optimize this solution and explore its application in broader scenarios to comprehensively enhance the security and effectiveness of machine learning.