Privacy-Preserving Machine Learning with Homomorphic Encryption: A Complete Benchmark for Medical Image Analysis - Machado, Lucas Castro Truppel; Idalino, Thaís Bardini (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg); 2026: Anais do XXVI Simpósio Brasileiro de CibersegurançaDO - 10.5753/sbseg.2026.29325, 2026)
Homomorphic encryption (HE) enables a remote server to perform Convolutional Neural Network (CNN) inference directly on encrypted medical images, but existing benchmarks often underreport deployment costs such as client-side cryptographic overhead and communication volume. This work presents an end-to-end benchmark of CKKS-encrypted CNN inference for binary pneumonia detection on PneumoniaMNIST, evaluated according to the seven-point reporting checklist proposed by Yanez and Yadav (2026). The evaluation covers plaintext-versus-encrypted classification quality, client-side key generation, encryption, and decryption on both a laptop and a Raspberry Pi 3 Model B+, server-side inference latency by operation, communication volume, edge-device energy consumption, and the assumed threat model under a CKKS parameter set providing at least 128 bits of classical security. The results show the main deployment barrier is not inference itself, but the one-time provisioning of evaluation keys, whose memory footprint exceeds the resources of typical edge devices and must therefore be delegated to a more capable host.