Benchmarking TenSEAL’s Homomorphic Encryption Through Predicting Encrypted RNA Sequencing Data

Abstract

This study addresses the growing need to protect sensitive healthcare data as digital technologies and cloud-based analytics become integral to modern medical research and care delivery. Healthcare data, such as clinical or genomic information, holds immense potential to enhance disease understanding and improve diagnostics through machine learning models; however, adopting third-party cloud technologies increases the risks of data breaches and noncompliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA). To address these concerns, this research investigates homomorphic encryption, a cryptographic method that allows computations on encrypted data without exposing sensitive information. The study benchmarks the TenSEAL library to evaluate its performance in encrypting healthcare test datasets and executing predictions through a pre-trained machine learning model, while also evaluating memory utilization and encryption time. The findings show that TenSEAL’s CKKS encryption scheme effectively enables data encryption and secure machine learning inference on genomic datasets for breast, lung, and prostate cancers, achieving an average accuracy of 90% across all datasets. On the other hand, our results also highlight a key trade-off: as encryption strength and dataset size increase, computational overhead rises sharply. Thus, medical professionals and data scientists must carefully balance the need for security with the practical deployment in real-world healthcare systems.