Thesis Details
Generative Adversarial Networks Applied for Privacy Preservation in Bio-Metric-Based Authentication and Identification
Biometric-based authentication systems are getting broadly adopted in many areas. However, these systems do not allow participating users to influence the way their data will be used. Furthermore, the data may leak and can be misused without the users' knowledge. In this thesis, we propose a new authentication method which preserves the privacy of an individual and is based on a generative adversarial network (GAN). Concretely, we suggest using the GAN for translating images of faces to a visually private domain (e.g., flowers or shoes). Classifiers, which are used for authentication purposes, are then trained on the images from the visually private domain. Based on our experiments, the method is robust against attacks and still provides meaningful utility.
privacy preservation, machine learning, generative adversarial networks, biometric systems
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Malinka Kamil, Mgr., Ph.D. (DITS FIT BUT), člen
Očenášek Pavel, Mgr. Ing., Ph.D. (DIFS FIT BUT), člen
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT), člen
@mastersthesis{FITMT22641,
author = "\'{L}ubo\v{s} Mjachky",
type = "Master's thesis",
title = "Generative Adversarial Networks Applied for Privacy Preservation in Bio-Metric-Based Authentication and Identification",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/22641/"
}