Tricycle: Private Transformer Inference with Tricyclic Encodings

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The growing deployment of large language models (LLMs) in privacy-sensitive settings demands inference mechanisms that preserve data confidentiality. Homomorphic encryption (HE) offers a principled solution by enabling computation directly on encrypted data; however, existing solutions struggle to scale to full LLMs. We present Tricycle, a system for efficient private transformer inference. At its core, Tricycle introduces tricyclic encodings, a novel packing scheme that enables batch matrix mul