2026-09-18T20:53:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1306082026-06-06T00:48:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Rus.
Stein, David Benjamin.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-05-14T16:29:11Z
2021-05-14T16:29:11Z
2020
2020
https://hdl.handle.net/1721.1/130608
1249684928
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).
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.
by David Benjamin Stein.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
106 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Efficient homomorphically encrypted privacy-preserving automated biometric classification
Thesis
application/pdf
2021-05-14T16:29:11Z
Publication
Master
EECS
unknown
unknown
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