Anonymization of high-dimensional data
Preserving privacy becomes inefficient due to the curse of dimensionality. The curse of dimensionality refers to a set of problems that arise when working with high-dimensional data. As the number of features or dimensions grows, the amount of data we need to generalize accurately grows exponentially. This is especially the case with k-anonymity: the more columns, the more complex reaching a k-anonymous dataset can be.
How does PCA work concerning the anonymization of datasets and dataset releases?
This exercise is part of the course
Data Privacy and Anonymization in Python
Hands-on interactive exercise
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