Predicting Genetic Disorder on Encrypted Data with Concrete ML Homomorphic Encryption Library

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dc.contributor.advisorHerendi, Tamás
dc.contributor.authorNguyen, Ngoc Hai Dang
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-06-26T20:47:21Z
dc.date.available2025-06-26T20:47:21Z
dc.date.created2025-04-17
dc.description.abstractThis paper investigates the use of Fully Homomorphic Encryption (FHE) through the ConcreteML library to ensure data privacy in machine learning applications. The research focuses on predicting genetic disorders using a Gradient Boosted Tree model trained on a dataset of 22,000 genomic samples. ConcreteML enables encrypted inference without exposing sensitive patient data during cloud-based processing. The results show that FHE-enabled models maintain high accuracy, comparable to their plaintext counterparts, with acceptable performance trade-offs. The thesis also discusses key technical challenges, including quantization, model compilation, and managing computational overhead. Ultimately, this work demonstrates the viability of secure, privacy-preserving machine learning in genomics using modern FHE tools.
dc.description.courseProgramtervező informatikus
dc.description.degreeMSc/MA
dc.format.extent57
dc.identifier.urihttps://hdl.handle.net/2437/394771
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectFully Homomorphic Encryption, Machine Learning
dc.subject.dspaceInformatics::Computer Science
dc.titlePredicting Genetic Disorder on Encrypted Data with Concrete ML Homomorphic Encryption Library
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