Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Privacy-Preserving Rare Disease Analysis With Fully Homomorphic Encryption(01. Izmir Institute of Technology, 2023) Erdoğmuş, Nesli; Akgün, Mete; Erdoğmuş, Nesli; Akgün, Mete; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyRare diseases severely affect many people across the world at the present time. Researchers conduct studies to understand the reasons behind rare diseases and as a result of this research, diagnosis, and treatment methods are developed. Rare disease analysis is performed to specify the disease-causing variants on the genome data of patients. The researchers need access to as much genome data as possible to find causing variants of rare diseases. On the other hand, the genome data of patients should be protected because it can be used to detect the identity of individuals. The researchers are not able to share the genome data of patients easily because of regulations such as General Data Protection Regulation (GDPR). For this reason, rare disease analysis should be performed in a secure way that protects the privacy of patients while enabling the collaboration of multiple medical institutions. In this context, a privacy-preserving collaborative system for rare disease analysis should be provided. This thesis study focuses on the utilization of fully homomorphic encryption, a method that enables unlimited number of operations to be performed on encrypted data, for privacy-preserving collaborative rare disease analysis. Two different methods, the boolean circuit method, and the integer arithmetic method, are implemented to perform rare disease analysis on the encrypted genome data to find disease-causing variants, and various experiments are performed to assess the efficiency of the proposed methods.Master Thesis Analysis of fingerprint matching performance with deep neural networks(01. Izmir Institute of Technology, 2022) Erdoğmuş, Nesli; Erdoğmuş, Nesli; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyFingerprints are unique biometric properties for each person. In the literature and industry, they are widely used for identification purposes. Collecting biometric datasets is a tedious work since it is not possible without the owners’ consent, and existing fingerprint datasets are either not sufficient to use in deep learning tasks by means of size or most of them are kept private to the collectors’ use. This increases the need of synthetic fingerprint images and their use in a variety of tasks especially for training deep learning models. In this study, the performance of a CNN architecture named Finger ConvNet[1] is compared to well-known networks and the question of whether a mixed dataset consisting of synthetically generated and real fingerprint images can reach a performance close or equal to ones having only real images is discussed. As a result of experiments, it is shown that the number of real images in the dataset is an important factor and that the performance of the mixed dataset was less than the one having only real images proposed in the referred study.Master Thesis Synthetic Fingerprint Generation With Gans(01. Izmir Institute of Technology, 2021) Kılınç, Vahdettin Onur; Erdoğmuş, Nesli; Erdoğmuş, Nesli; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of EngineeringFingerprints regarded as the most reliable form of human identification for thousands of years. Even though the fingerprint collection process became more convenient with technological advancements, privacy concerns slowed down the researchers working on fingerprint biometrics. Like every other problem solved with deep learning, biometrics requires a sizable database to succeed. This study generates synthetic fingerprints to tackle bottlenecks created by privacy laws. First, the pipeline designed enhances images from a small publicly available fingerprint dataset. The new enhanced dataset is given as an input to a generative network to create candidate synthetic fingerprints. Fingerprint image quality models choose high-quality fingerprint images from the candidate set to form the synthetic fingerprint dataset. Numerous experiments were conducted to show the quality of the generated synthetics fingerprints using both real and synthetic fingerprint datasets available.Experimental results show that enhancing fingerprint images from real-life datasets helps integrate synthetic fingerprint images into real life. Synthetic fingerprints generated from the pipeline can generate large datasets with a representative quality close to their real-life counterparts without privacy concerns.