Practical Homomorphic LSTM via Programmable Bootstrapping

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While deep learning is ubiquitous, centralized pro- cessing exposes sensitive sequential data—such as natural lan- guage—to untrusted servers, forcing an unacceptable privacy- utility trade-off. Fully Homomorphic Encryption (FHE) re- solves this by computing directly on encrypted data. However, standard neural networks ported to FHE suffer from severe latency bottlenecks, particularly because continuous non-linear activations dominate the computational budget. To overcome this, we introd