Description: Non-linear transformation involves changing data in a way that does not preserve linear relationships, which can enhance data anonymization. This approach is based on the idea that relationships between data are not always linear, and by applying complex transformations, patterns that could be used to identify individuals can be obscured. Non-linear transformations may include techniques such as rotation, distortion, and projection into high-dimensional spaces, allowing for greater variability in the resulting data. This is especially relevant in the context of data anonymization, where privacy protection is crucial. By transforming data non-linearly, it becomes more challenging to correlate sensitive information with specific individuals, aiding in compliance with privacy regulations and protecting user identity. In the field of machine learning, non-linear transformations are essential for enhancing the capabilities of algorithms, enabling these models to recognize complex patterns in various types of data. In summary, non-linear transformation is a powerful technique that plays a crucial role in data protection and the advancement of artificial intelligence.
