Hybrid Privacy-preserving Histopathological Image Classification Using Fully Homomorphic Encryption

Abstract

Automated analysis of histopathological scans allows for cancer diagnosis, yet deploying deep learning models for automated classification brings the risk of misuse of patient data and privacy violations. Fully homomorphic encryption (FHE) is a cryptographic paradigm that allows for computation to be performed on encrypted data by untrusted parties, without decrypting the data. While FHE solves the problem of preserving privacy for remote computation on sensitive data, it is prohibitively computationally expensive when used with deep neural networks. To address this bottleneck, this study proposes a hybrid architecture that distributes computation between the client and the server. Our method utilizes a pretrained DINO ViT model for local image feature extraction on the client, followed by dimensionality reduction using the principal and neighborhood component analysis methods. These reduced features are then encrypted and classified remotely by a support vector machine (SVM), that can be executed in untrusted environments using FHE. We evaluated this approach on demonstrating that feature dimensions can be reduced by approximately 50% with less than one percentage point decrease in classification accuracy, while dramatically reducing encrypted model inference time.