AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning

TL;DR

This paper explores how machine learning algorithms, particularly differential privacy, can be used to enhance personal data anonymization and privacy protection in the era of AI-driven data analysis.

Contribution

It introduces a differential privacy-based machine learning approach for personal data anonymization and discusses challenges and improvements in privacy protection methods.

Findings

Effective differential privacy algorithms for data anonymization

Identification of key factors affecting privacy protection

Proposed improvements for dataset privacy detection

Abstract

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.