PrivFT: Private and Fast Text Classification with Homomorphic Encryption
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Privacy and security have increasingly become a concern for computing services in recent years. In this work, we present an efficient method for Text Classification while preserving the privacy of the content, using Fully Homomorphic Encryption (FHE). We train a simple supervised model on unencrypted data to achieve competitive results with recent approaches and outline a system for performing inferences directly on encrypted data with zero loss to prediction accuracy. This system is implemented with GPU hardware acceleration to achieve a run time per inference of less than 0.66 seconds, resulting in more than 12× speedup over its CPU counterpart. Finally, we show how to train this model from scratch using fully encrypted data to generate an encrypted model.
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