Distributed Homomorphic Anomaly Detection

Proof-of-concept ML algorithms to do anomaly detection compatible with distributed, encrypted algorithms.

The goal of this project is prove that state-of-the-art anomaly detection algorithms that already achieve over 99% accuracy can be run in a distributed and encrypted fashion. In other words, prove that multiple parties can collectively train a good anomaly detecting model without revealing their data to each other. This has been achieved through transfer learning into a Multi-layer Perceptron (MLP) model which achieved comparable results (95% compared to 99% False-Positive Rate accuracy).