Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging.

Abstract

Purpose To evaluate the technical feasibility of implementing homomorphic encryption in deep learning models for privacy-preserving CT image analysis of renal masses. Materials and Methods A privacy-preserving deep learning system was developed through three sequential technical phases: a reference CNN model (Ref-CNN) based on ResNet architecture, modification for encryption compatibility (Approx-CNN) by replacing ReLU with polynomial approximation and max-pooling with averagepooling, and implementation of fully homomorphic encryption (HE-CNN). The CKKS encryption scheme was used for its capability to perform arithmetic operations on encrypted real numbers. Using 12,446 CT images from a public dataset (3,709 renal cysts, 5,077 normal kidneys, and 2,283 kidney tumors), we evaluated model performance using area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC). Results All models demonstrated high diagnostic accuracy with AUC ranging from 0.89-0.99 and AUPRC from 0.67-0.99. The diagnostic performance trade-off was minimal from Ref-CNN to Approx-CNN (AUC: 0.99 to 0.97 for normal category), with no evidence of differences between models. However, encryption significantly increased storage and computational demands: a 256 × 256-pixel image expanded from 65KB to 32MB, requiring 50 minutes for CPU inference but only 90 seconds with GPU acceleration. Conclusion This technical development demonstrates that privacy-preserving deep learning inference using homomorphic encryption is feasible for renal mass classification on CT images, achieving comparable diagnostic performance while maintaining data privacy through end-to-end encryption. ©RSNA, 2025.