Técnicas esenciales para anonimizar archivos de Excel antes de su análisis con IA
Técnicas esenciales para anonimizar archivos de Excel antes de su análisis con IA
The fundamental distinction between anonymization, which is irreversible and ensures non-identification, and pseudonymization, which is reversible, is crucial for the protection of personal data. This difference defines the level of security and flexibility in processing operations, establishing that only true anonymization definitively eliminates the risks of re-identification, whereas pseudonymization maintains a layer of control under specific conditions. To responsibly feed artificial intelligence systems, it is mandatory to apply these techniques to comply with privacy regulations and avoid legal risks. The integration of methods such as code substitution, visual masking, and the introduction of random noise allows AI models to learn from meaningful patterns without accessing individually identifiable information, thereby preserving user trust and organizational reputation. This article is relevant to open data because it establishes the ethical and technical foundations for sharing datasets without compromising privacy. It demonstrates that the quality and utility of open data do not depend on the exposure of identity, but rather on the rigor of prior transformation and anonymization. By normalizing these practices, it facilitates the creation of more secure and scalable data ecosystems, where technological innovation advances without violating individuals' fundamental rights.
Source: wwwhatsnew.comPublished on 2025-05-20