Privacy Preserving Biometric Multi-factor Authentication
(2023) EITM01 20231Department of Electrical and Information Technology
- Abstract
- This thesis investigates the viability of using Fully Homomorphic Encryption and Machine Learning to construct a privacy-preserving biometric multi-factor authentication system. The system is based on the architecture described as ”Model K
- Store distributed, compare distributed” in ISO/IEC 24745:2022 and uses the Torus Fully Homomorphic Encryption scheme proposed by Chillotti et al. (Journal of Cryptology, 2020) to encode and compare encrypted fingerprint images. A machine-learning-based encoder is designed using the VGG11 network architecture described by Simonyan and Zisserman (2015). The encoder is tuned
for one-shot classification as a Siamese network to optimize the Euclidean distance between fingerprints from different... (More) - This thesis investigates the viability of using Fully Homomorphic Encryption and Machine Learning to construct a privacy-preserving biometric multi-factor authentication system. The system is based on the architecture described as ”Model K
- Store distributed, compare distributed” in ISO/IEC 24745:2022 and uses the Torus Fully Homomorphic Encryption scheme proposed by Chillotti et al. (Journal of Cryptology, 2020) to encode and compare encrypted fingerprint images. A machine-learning-based encoder is designed using the VGG11 network architecture described by Simonyan and Zisserman (2015). The encoder is tuned
for one-shot classification as a Siamese network to optimize the Euclidean distance between fingerprints from different individuals. The network is then made compatible with TFHE using the Concrete-ml library for Python.
Using a prototype of the system, we show that the system succeeds in preserving users’ privacy with a relatively high authentication success rate. However, performance benchmarks show that the proposed encoding method is too inefficient. Finally, we highlight some areas of interest for future work that could make a system for privacy-preserving biometric multi-factor authentication viable. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9131800
- author
- Gedenryd, Emil LU
- supervisor
-
- Qian Guo LU
- organization
- course
- EITM01 20231
- year
- 2023
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Fully Homomorphic Encryption, Machine Learning, Biometric Authentication
- report number
- LU/LTH-EIT 2023-935
- language
- English
- id
- 9131800
- date added to LUP
- 2023-07-04 11:19:05
- date last changed
- 2023-07-04 11:19:05
@misc{9131800,
abstract = {{This thesis investigates the viability of using Fully Homomorphic Encryption and Machine Learning to construct a privacy-preserving biometric multi-factor authentication system. The system is based on the architecture described as ”Model K
- Store distributed, compare distributed” in ISO/IEC 24745:2022 and uses the Torus Fully Homomorphic Encryption scheme proposed by Chillotti et al. (Journal of Cryptology, 2020) to encode and compare encrypted fingerprint images. A machine-learning-based encoder is designed using the VGG11 network architecture described by Simonyan and Zisserman (2015). The encoder is tuned
for one-shot classification as a Siamese network to optimize the Euclidean distance between fingerprints from different individuals. The network is then made compatible with TFHE using the Concrete-ml library for Python.
Using a prototype of the system, we show that the system succeeds in preserving users’ privacy with a relatively high authentication success rate. However, performance benchmarks show that the proposed encoding method is too inefficient. Finally, we highlight some areas of interest for future work that could make a system for privacy-preserving biometric multi-factor authentication viable.}},
author = {{Gedenryd, Emil}},
language = {{eng}},
note = {{Student Paper}},
title = {{Privacy Preserving Biometric Multi-factor Authentication}},
year = {{2023}},
}