  Knowledge discovery is one of the main goals of Artificial Intelligence. This
Knowledge is usually stored in databases spread in different environments,
being a tedious (or impossible) task to access and extract data from them. To
this difficulty we must add that these datasources may contain private data,
therefore the information can never leave the source. Privacy Preserving
Machine Learning (PPML) helps to overcome this difficulty, employing
cryptographic techniques, allowing knowledge discovery while ensuring data
privacy. K-means is one of the data mining techniques used in order to discover
knowledge, grouping data points in clusters that contain similar features. This
paper focuses in Privacy Preserving Machine Learning applied to K-means using
recent protocols from the field of criptography. The algorithm is applied to
different scenarios where data may be distributed either horizontally or
vertically.
