Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
This work addresses privacy concerns in cloud-based ML services by enabling practical and private inference for users and model owners, though it is an incremental improvement over existing hybrid FHE methods.
The paper tackles the challenge of making fully homomorphic encryption (FHE) practical for machine learning inference by introducing Safhire, a hybrid framework that offloads non-linear activations to the client to eliminate bootstrapping and support exact activations, resulting in 1.5X to 10.5X lower inference latency compared to a state-of-the-art baseline.
In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. Fully homomorphic encryption (FHE) enables inference directly on encrypted inputs, but its practicality is hindered by expensive bootstrapping and inefficient approximations of non-linear activations. We introduce Safhire, a hybrid inference framework that executes linear layers under encryption on the server while offloading non-linearities to the client in plaintext. This design eliminates bootstrapping, supports exact activations, and significantly reduces computation. To safeguard model confidentiality despite client access to intermediate outputs, Safhire applies randomized shuffling, which obfuscates intermediate values and makes it practically impossible to reconstruct the model. To further reduce latency, Safhire incorporates advanced optimizations such as fast ciphertext packing and partial extraction. Evaluations on multiple standard models and datasets show that Safhire achieves 1.5X - 10.5X lower inference latency than Orion, a state-of-the-art baseline, with manageable communication overhead and comparable accuracy, thereby establishing the practicality of hybrid FHE inference.