Efficient homomorphically encrypted privacy-preserving automated biometric classification
Name
1249684928-MIT.pdf
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2.31 MB
Format
Adobe PDF
Checksum (MD5)
fbf51738753bed77c15bad9d274f83f5
Author(s)
Stein, David Benjamin.
Advisor(s)
Daniela Rus.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 87-96).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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