Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1Prism: Privacy-Preserving Rare Disease Analysis Using Fully Homomorphic Encryption(Oxford Univ Press, 2025) Akkaya, Guliz; Erdogmus, Nesli; Akgun, MeteMotivation Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants.Results We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17x improvement in runtime for recessive/dominant filtering and 22x for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting.Availability and implementation The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.Article Regression Via Classification for Fingerprint Orientation Estimation(Ieee-inst Electrical Electronics Engineers inc, 2024) Erdogmus, NesliEstimating the direction in which the ridges and valleys of the fingerprint pattern are aligned often serves as a pivotal first step in fingerprint recognition systems. The ridge orientation map is a fundamental reference for subsequent processing stages, such as image enhancement, feature extraction, and matching. Therefore, its accuracy is essential to achieve high recognition rates. Ridge orientation estimation entails a regression problem since the task is to estimate an angle between 0 degrees and 180 degrees for each sub-region in the fingerprint image. However, the majority of the approaches in the literature pivot towards framing this regression task as a classification problem. This paper systematically analyzes the regression via classification methodology for fingerprint orientation estimation, exploring various discretization and encoding strategies. Specifically, we examine single and multiple discretization schemes designed to ensure that resulting bins maintain uniform length or uniform probability or are allocated randomly, paired with one-hot, ordinal, and cyclic encoding techniques. Our experiments are conducted on the FOE-TEST database from FVC-onGoing, the sole publicly available fingerprint orientation dataset. The findings highlight the efficacy of cyclic encoding over the one-hot encoding prevalent in prior research, while equal-length and equal-probability discretization strategies yield comparable results.