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.

中文翻译：

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隐私保护 K-Means 聚类：一种安全的多方计算方法

知识发现是人工智能的主要目标之一。这些知识通常存储在分布在不同环境中的数据库中，从数据库中访问和提取数据是一项乏味（或不可能）的任务。对于这个困难，我们必须补充一点，这些数据源可能包含私有数据，因此信息永远不会离开源。隐私保护机器学习 (PPML​​) 有助于克服这一困难，它采用加密技术，在确保数据隐私的同时允许知识发现。K-means 是一种数据挖掘技术，用于发现知识，将数据点分组到包含相似特征的集群中。本文重点介绍了使用加密领域的最新协议应用于 K-means 的隐私保护机器学习。