Description: Multi-source Anonymization is an approach that combines data from multiple sources while ensuring the anonymity of the individuals involved. This process is fundamental in the information age, where data collection and analysis have become ubiquitous. Anonymization involves transforming personal data in such a way that individuals cannot be identified from it, which is crucial for protecting privacy and complying with regulations like GDPR. Multi-source Anonymization is distinguished by its ability to integrate data from various databases, such as medical records, social media data, and commercial transactions, ensuring that the combined information does not reveal individuals’ identities. This approach not only enhances the quality and richness of the analyzed data but also allows organizations to gain valuable insights without compromising privacy. Techniques used may include generalization, perturbation, and masking, which help maintain the utility of the data while minimizing re-identification risks. In a world where data protection is increasingly critical, Multi-source Anonymization emerges as an effective solution to balance the need for data analysis with the obligation to protect individuals’ privacy.
