A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference
Rising privacy concerns around large language models have accelerated research in private Transformer inference (PTI).
ProofPaper ↗
Key points
- 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).
- 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.
Sources (1)
- [1]A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer InferencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:30 AM
Rising privacy concerns around large language models have accelerated research in private Transformer inference (PTI).
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.
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