Privacy-Preserving Brain Tumor Detection Using Fully Homomorphic Encryption: A Comparative Evaluation of TenSEAL and Concrete ML

Abstract

As privacy concerns grow alongside the increasing use of deep learning (DL) in healthcare, Fully Homomorphic Encryption (FHE) presents a viable solution for maintaining data confidentiality while enabling encrypted computation. This study explores the implementation of encrypted Convolutional Neural Networks (CNNs) for brain tumor detection within a secure client-server system. Two leading FHE libraries—TenSEAL and Concrete ML—were evaluated in terms of classification accuracy, runtime efficiency, and integration feasibility. The TenSEALbased CNN preserved its plaintext accuracy (75%) after encryption, while the Concrete ML model experienced a slight accuracy drop (from 82.5% to 75%). Despite comparable runtime performance, TenSEAL’s consistent results and more transparent parameter tuning made it the preferred choice for deployment. This work contributes a novel use case of FHE in medical imaging beyond the standard MNIST dataset and provides actionable insights for future implementations of privacy-preserving machine learning in healthcare.