Abstract
This tutorial chapter provides a comprehensive guide to implementing privacy-preserving Support Vector Machine (SVM) inference using Fully Homomorphic Encryption (FHE). We demonstrate a practical solution for secure and private SVM inference on encrypted data, enabling sensitive data analysis while maintaining confidentiality. Through a step-by-step implementation on a real-world dataset, we cover data preparation, SVM model training, and homomorphic inference. Our experimental results on a commodity laptop show that our approach achieves high accuracy with a reasonable latency of nearly 6 seconds for SVM inference. This chapter serves as a valuable resource for practitioners and researchers seeking to apply privacy-preserving techniques to SVM solutions, with significant implications for applications like medical diagnosis, financial prediction, and recommender systems, where data privacy is crucial. By following this tutorial, readers can gain hands-on experience with privacy-preserving SVM inference using FHE.