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).
active
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entered showcase: 2024-01-03
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entry updated: 2024-04-09
This project has not yet been evaluated by the C4DT Factory team.
We will be happy to evaluate it upon request.
Experiments
Python