Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach

TL;DR

This paper presents a cryptographic protocol for privacy-preserving K-means clustering that enables data analysis across distributed sources without compromising individual data privacy.

Contribution

It introduces a secure multi-party computation approach for K-means clustering applicable to horizontally and vertically distributed data sources.

Findings

Effective privacy preservation in distributed K-means clustering

Applicable to both horizontal and vertical data distributions

Maintains clustering accuracy while ensuring data confidentiality

Abstract

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.