PRIVACY-PRESERVING PHISHING WEB PAGE CLASSIFICATION VIA FULLY HOMOMORPHIC ENCRYPTION

This work introduces a fast and lightweight homomorphic-encryption pipeline that enables privacy-preserving machine learning for phishing web page recognition. The primary goals are to use visual features to train an accurate model and to implement an inference pipeline with practical runtime and communication costs. To do so, we deploy a variety of techniques that cover deep learning and optical character recognition to extract salient visual features, and optimize the inner mechanisms of state-of-the-art homomorphic encryption schemes to reduce the encryption-related costs. Our presented system is able to achieve over 90% on the visual classification task, while using less than 250 KB of communication bandwidth and around 0.7 seconds of computation time. We hope our work not only demonstrates a private visual phishing detection pipeline, but also outlines techniques to practically utilize homomorphic encryption in a variety of machine learning tasks.