Description: Label-based anonymization is a technique that uses labels to categorize and anonymize data, allowing sensitive information to be protected while maintaining its utility for analysis and studies. This methodology involves assigning labels to data, which can be classified into different levels of sensitivity or categories, facilitating the identification of which data can be shared without compromising individual privacy. Through this process, the aim is to minimize the risk of re-identification of data, ensuring that personal information cannot be linked to a specific individual. Label-based anonymization is particularly relevant in contexts where large volumes of data are handled, such as in various sectors including healthcare, academic research, and business data analysis. Furthermore, this technique aligns with data protection regulations, such as the GDPR in Europe, which require organizations to implement adequate measures to protect personal data privacy. In summary, label-based anonymization not only helps comply with privacy regulations but also allows organizations to responsibly and ethically leverage data.
