AI-Driven Anonymization: Protecting Personal Data Privacy While
  Leveraging Machine Learning
Le Yang Miao Tian Duan Xin Qishuo Cheng Jiajian Zheng
The development of artificial intelligence has significantly transformed
people's lives. However, it has also posed a significant threat to privacy and
security, with numerous instances of personal information being exposed online
and reports of criminal attacks and theft. Consequently, the need to achieve
intelligent protection of personal information through machine learning
algorithms has become a paramount concern. Artificial intelligence leverages
advanced algorithms and technologies to effectively encrypt and anonymize
personal data, enabling valuable data analysis and utilization while
safeguarding privacy. This paper focuses on personal data privacy protection
and the promotion of anonymity as its core research objectives. It achieves
personal data privacy protection and detection through the use of machine
learning's differential privacy protection algorithm. The paper also addresses
existing challenges in machine learning related to privacy and personal data
protection, offers improvement suggestions, and analyzes factors impacting
datasets to enable timely personal data privacy detection and protection.