Fully Homomorphic Encryption and k-Nearest Neighbour Classification

Kiowa Scott-Hurley1, Chris Watkins1

1CSIRO, Clayton South, VIC

 

The ability to perform encrypted computation on encrypted data enables a range of cloud and edge based computing solutions to be applied to sensitive data, either at scale or closer to the data source. We implemented a k-nearest neighbours classifier in a ho-momorphically encrypted space using the Microsoft SEAL library. The scheme imagines a user and cloud scenario, in which multiple users cooperatively train a classifier on their combined encrypted data without sharing the data with one another, to motivate the use of encrypted computation. We demonstrate near linear performance results on large datasets (16,000 points) across a range of model parameters. This implementation illustrates that fully homomorphically encrypted machine learning is no longer prohibitively slow, and opens a pathway to encrypt other machine learning techniques in the future.


Biography:

Kiowa Scott-Hurley is a cadet with the Scientific Computing team at CSIRO. A student of philosophy and pure mathematics Kiowa has been applying her high level reasoning and complex problem solving skills to challenges in modern post quantum safe cryptography and machine learning

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