# Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC

arXiv: https://arxiv.org/abs/2508.19525 · HTML: https://mlsanity.com/paper/2508.19525/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2025.

## Built on by (most cited first, 7 of 7)

- [Minim: Privacy-Aware Minimal View for Agents via Trusted Local Sanitization](https://mlsanity.com/paper/2606.13949/) (2026)
- [Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings](https://mlsanity.com/paper/2609.03376/) (2026)
- [UFO: Unlocking Ultra-Efficient Quantized Private Inference with Protocol and Algorithm Co-Optimization](https://mlsanity.com/paper/2602.18758/) (2026)
- [Gecko: Fast Private Inference via Secure Public Encoder Offloading](https://mlsanity.com/paper/2608.02378/) (2026)
- [Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation](https://mlsanity.com/paper/2609.10264/) (2026)
- [OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design](https://mlsanity.com/paper/2609.16898/) (2026)
- [A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference](https://mlsanity.com/paper/2610.19607/) (2026)

## Abstract (arXiv)

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× reduction in communication overhead compared to BOLT (S&P'24) and a 2× reduction compared to Bumblebee (NDSS'25), along with latency reductions of 13× and 1.8×, respectively, when leveraging GPU acceleration.