Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Edu-Voting: an Educational Homomorphic E-Voting System(Karabük Üniversitesi, 2018) Tekin, Leyla; Özgür, Hüseyin Güven; Sayin, Burcu; Karataş, Arzum; Şenkula, Pelin; İrtem, Emre; Şahin, SerapAs an instrument of democracy, voting is a critical issue. Although paper-based voting systems are still used commonly, e-voting systems have started to substitute under favor of improvements in the technology. This situation gives rise to need for secure, reliable, and transparent e-voting systems to make people trust. To do this, there are some security requirements that should be concerned and satisfied such as privacy, fairness, verifiability etc. This study has an educational intuition that analyzes those requirements, theoretical background information related to cryptographic schemes behind them and creates a place-based e-voting design which was implemented for kiosk voting. As a contribution, Paillier homomorphic cryptosystem is used in our system. Moreover, our study includes a detailed criticism for the implemented system in terms of chosen cryptosystems and design modules with security and e-voting requirements.Conference Object Citation - WoS: 2Citation - Scopus: 3Impact of Variations in Synthetic Training Data on Fingerprint Classification(IEEE, 2019) İrtem, Pelin; İrtem, Emre; Erdoğmuş, NesliCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.