Differential Privacy Technology of Big Data Information Security based on ACA-DMLP

computing and artificial intelligence have a deeper and closer connection with daily life. To ensure information security, most companies or individuals choose to pay a simple fee to store a large amount of data on cloud servers and hand over a large number of complex calculations of machine learning to cloud servers. To eliminate the security risks of data stored in the cloud and ensure that private data is not leaked, this paper proposes a collusion-resistant distributed machine learning scheme. Through homomorphic encryption algorithm and differential privacy algorithm, the security of data and model in machine learning framework is guaranteed. The distributed machine learning framework is adopted to reduce the data computing time and improve the data training efficiency. The simulation results show that the computational efficiency is improved while the user privacy security is guaranteed. The accuracy of model training is not reduced due to the improvement of privacy data security and computational efficiency. Through this study, we can further propose effective measures for the privacy protection of outsourced data and the data integrity of machine learning, which is of great significance to the security research of cloud intelligent big data.