mlsanityData to 7 Oct 2026

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

arXiv 2402.17191 ↗First posted 27 Feb 2024cs.CR cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 1 of its 23 references that are in the map. First posted on arXiv 27 Feb 2024.

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1 of 23 references.

References · 1

  1. 2024Application analysis of ai technology combined with spiral CT scanning in early lung cancer screeningcited by 3

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.