Privacy-Preserving Classification of Medical Tabular Data with Homomorphic Encryption

Machine learning (ML) offers significant potential for disease prediction, clinical decision support, and medical data classification, but its reliance on sensitive patient data raises privacy and security concerns, particularly under strict healthcare regulations. Traditional encryption methods require data to be decrypted prior to computation, such as in ML workflows, thereby introducing risks of exposure and undermining data confidentiality. Homomorphic Encryption (HE) addresses this challenge by enabling computations directly on encrypted data, ensuring end-to-end privacy. This paper explores the integration of the Cheon-Kim-Kim-Song (CKKS) HE scheme into the inference phase of medical tabular data classification. We evaluate the performance of Logistic Regression (LR), Support Vector Machine (SVM), and a lightweight multilayer perceptron (MLP) under HE-based inference, and compare their classification accuracy, computational overhead, and latency against plaintext counterparts. Additionally, we propose two hybrid models (LR-MLP and SVM-MLP) to accelerate training convergence and enhance inference performance. Experimental results demonstrate that while HE-based inference introduces moderate computational cost and data transmission overheads, it maintains accuracy comparable to plaintext inference. These outcomes affirm the practical feasibility of HE for privacy-preserving machine learning in healthcare, while also highlighting key implementation trade-offs. Furthermore, the findings support the advancement of secure AI systems and promote the adoption of cryptographic techniques in digital health and other privacy-critical fields.