Welcome to the resource topic for 2025/1532
Title:
Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC
Authors: Tianshi Xu, Wen-jie Lu, Jiangrui Yu, Yi Chen, Chenqi Lin, Runsheng Wang, Meng Li
Abstract:This paper presents an efficient framework for private Transformer inference that combines Homomorphic Encryption (HE) and Secure Multi-party Computation (MPC) to protect data privacy. Existing methods often leverage HE for linear layers (e.g., matrix multiplications) and MPC for non-linear layers (e.g., Softmax activation functions), but the conversion between HE and MPC introduces significant communication costs. The proposed framework, dubbed BLB, overcomes this by breaking down layers into fine-grained operators and further fusing adjacent linear operators, reducing the need for HE/MPC conversions. To manage the increased ciphertext bit width from the fused linear operators, BLB proposes the first secure conversion protocol between CKKS and MPC and enables CKKS-based computation of the fused operators. Additionally, BLB proposes an efficient matrix multiplication protocol for fused computation in Transformers. Extensive evaluations on BERT-base, BERT-large, and GPT2-base show that BLB achieves a 21\times reduction in communication overhead compared to BOLT (S&P’24) and a 2\times reduction compared to Bumblebee (NDSS’25), along with latency reductions of 13\times and 1.8\times, respectively, when leveraging GPU acceleration.
ePrint: https://eprint.iacr.org/2025/1532
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