Efficient homomorphically encrypted privacy-preserving automated biometric classification

Abstract

This thesis investigates whether biometric recognition can be performed on encrypted data without decrypting the data. Borrowing the concept from machine learning, we develop approaches that cache as much computation as possible to a pre-computation step, allowing for efficient, homomorphically encrypted biometric recognition. We demonstrate two algorithms: an improved version of the k-ishNN algorithm originally designed by Shaul et. al. in [1] and a homomorphically encrypted implementation of a SVM classifier. We provide experimental demonstrations of the accuracy and practical efficiency of both of these algorithms.