BLB combines operator-level fusion with a new secure CKKS-to-MPC conversion, reporting up to 21x less communication and 13x lower GPU latency than BOLT for private Transformer inference.
Accelerating Private Large Transformers Inference through Fine-grained Collaborative Computation
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abstract
Homomorphic encryption (HE) and secret sharing (SS) enable computations on encrypted data, providing significant privacy benefits for large transformer-based models (TBM) in sensitive sectors like medicine and finance. However, private TBM inference incurs significant costs due to the coarse-grained application of HE and SS. We present FASTLMPI, a new approach to accelerate private TBM inference through fine-grained computation optimization. Specifically, through the fine-grained co-design of homomorphic encryption and secret sharing, FASTLMPI achieves efficient protocols for matrix multiplication, SoftMax, LayerNorm, and GeLU. In addition, FASTLMPI introduces a precise segmented approximation technique for differentiable non-linear, improving its fitting accuracy while maintaining a low polynomial degree. Compared to solution BOLT (S&P'24), FASTLMPI shows a remarkable 54% to 64% decrease in runtime and an impressive 72.2% reduction in communication costs.
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Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC
BLB combines operator-level fusion with a new secure CKKS-to-MPC conversion, reporting up to 21x less communication and 13x lower GPU latency than BOLT for private Transformer inference.