ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

2021/10/21 by Ş. S. Mağara, Şeyma Selcan Mağara, Mağara, Ş. S. +27
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cancer Genomics and Diagnostics #Chaos-based Image/Signal Encryption #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #cs.CR #cs.LG #q-bio.GN

paper · pdf · doi:10.48550/arxiv.2110.11446

openalex publication_date 2021/10/21 · arxiv created 2022/02/01 · arxiv updated 2022/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.

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