# A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference

arXiv: https://arxiv.org/abs/2610.19607 · HTML: https://mlsanity.com/paper/2610.19607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 87 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [MPCFormer: fast, performant and private Transformer inference with MPC](https://mlsanity.com/paper/2211.01452/) (2022)
- [THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption](https://mlsanity.com/paper/2206.00216/) (2022)
- [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://mlsanity.com/paper/2307.12533/) (2023)
- [LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers](https://mlsanity.com/paper/2305.18396/) (2023)
- [MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention](https://mlsanity.com/paper/2211.13955/) (2022)
- [Primer: Fast Private Transformer Inference on Encrypted Data](https://mlsanity.com/paper/2303.13679/) (2023)
- [Encryption-Friendly LLM Architecture](https://mlsanity.com/paper/2410.02486/) (2024)
- [Nimbus: Secure and Efficient Two-Party Inference for Transformers](https://mlsanity.com/paper/2411.15707/) (2024)
- [PermLLM: Private Inference of Large Language Models within 3 Seconds under WAN](https://mlsanity.com/paper/2405.18744/) (2024)
- [CipherPrune: Efficient and Scalable Private Transformer Inference](https://mlsanity.com/paper/2502.16782/) (2025)
- [MERGE: Fast Private Text Generation](https://mlsanity.com/paper/2305.15769/) (2023)
- [SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC](https://mlsanity.com/paper/2401.00793/) (2024)
- [Entropy-Guided Attention for Private LLMs](https://mlsanity.com/paper/2501.03489/) (2025)
- [Ditto: Quantization-aware Secure Inference of Transformers upon MPC](https://mlsanity.com/paper/2405.05525/) (2024)
- [Power-Softmax: Towards Secure LLM Inference over Encrypted Data](https://mlsanity.com/paper/2410.09457/) (2024)
- [Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC](https://mlsanity.com/paper/2508.19525/) (2025)
- [Secure Transformer Inference Protocol](https://mlsanity.com/paper/2312.00025/) (2023)
- [MPC-Minimized Secure LLM Inference](https://mlsanity.com/paper/2408.03561/) (2024)
- [Efficient Decoding Methods for Language Models on Encrypted Data](https://mlsanity.com/paper/2509.08383/) (2025)
- [CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing](https://mlsanity.com/paper/2511.01197/) (2025)
- [Cachemir: Fully Homomorphic Encrypted Inference of Generative Large Language Model with KV Cache](https://mlsanity.com/paper/2602.11470/) (2026)
- [Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity](https://mlsanity.com/paper/2505.07239/) (2025)
- [What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference](https://mlsanity.com/paper/2605.23158/) (2026)
- [SoK: Privacy-preserving Deep Learning with Homomorphic Encryption](https://mlsanity.com/paper/2112.12855/) (2021)
- [A Survey on Private Transformer Inference](https://mlsanity.com/paper/2412.08145/) (2024)
- [MPCache: MPC-Friendly KV Cache Eviction for Efficient Private LLM Inference](https://mlsanity.com/paper/2501.06807/) (2025)
- [CryptoGen: Secure Transformer Generation with Encrypted KV-Cache Reuse](https://mlsanity.com/paper/2602.08798/) (2026)

## Abstract (arXiv)

Rising privacy concerns around large language models have accelerated research in private Transformer inference (PTI). Synthesizing the empirical claims in this line of research, however, is hindered by multi-dimensional cryptographic progress and inconsistent evaluation contracts. In this work, we systematize the field by constructing an empirical graph of 62 PTI systems, mapping system attributes to node features and evaluation compatibility to edges. A three-tiered analysis of this graph illuminates the state of private inference. Node-level attribution shows that cryptographic progress drives performance gains across most systems, yet always co-occurs with other factors (model fidelity, hardware, cost accounting). Edge-level auditing shows that only 1 of 1891 candidate system pairs is directly comparable under shared assumptions. Finally, graph-level analysis shows that the scaling frontier of private inference has plateaued at 13B parameters and projects the cost of deploying current PTI techniques on production-scale Transformers. Overall, this graph-based framework equips the community to evaluate PTI claims transparently and channel technical speedups into deployment-scale private inference.