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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?

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Data Privacy and Anonymization in Python

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