Homomorphic Encryption

A Visual Metaphor

Richard Craib, the creator of Numerai, introduced me to homomorphic encryption in 2015. I was excited, I’d felt a need for data to be analyzed without being compromised, that is what homomorphic encryption allows.

It is a complicated, abstract concept that I would like to break down using some animated visual metaphors:

Homomorphic encryption allows confidential data to be obscured, but still analyzed without it being made public

Imagine the word search below is a confidential data set, the words contained within are valuable signals that can be extracted from the noise.

When homomorphically encrypted, the data is obscured, but the structure of it is not

Anyone in the world could find proverbial words in the word search, extract the valuable signal from the noise, without seeing the original data.

The owner of the confidential data can apply findings in the homomorphically encrypted data to the confidential data

Anyone in the world could find proverbial words in the word search, extract the valuable signal from the noise, without seeing the original content

Numerai is applying this technology to the model of a hedge fund, allowing anyone in the world to analyse private financial data sets and get a cut of the returns. This isn’t the only use for the technology though. It could be applied to things like electronic voting or analyzing healthcare data without risking patient privacy. Apple are playing with similar techniques also.

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