Efficient homomorphically encrypted privacy-preserving automated biometric classification

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Stein, David Benjamin.

Advisor dc:contributor.advisor

• Daniela Rus.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130608

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130608