# Shuffling Is Not Enough: Breaking Permutation-Based Model Confidentiality

> Source: <https://arxiv.org/abs/2609.12911>
> Published: 2026-09-15 04:07:08+00:00

# Computer Science > Cryptography and Security

  [Submitted on 11 Sep 2026]

# Title:Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference

[View PDF](https://arxiv.org/pdf/2609.12911)

[HTML (experimental)](https://arxiv.org/html/2609.12911v1)

Abstract:Hybrid fully homomorphic encryption~(FHE) inference improves the practicality of private inference by letting the server evaluate linear layers homomorphically while the client decrypts and applies nonlinearities. Recent schemes attempt to protect model confidentiality by returning noisy, output-permuted responses and appealing to shuffle-model differential privacy~(DP). We show that this protection fails in the correctness regime required by hybrid FHE systems. For a $d$-input linear layer, $d+1$ admissible queries suffice for exact recovery of a permutation-invariant layer summary, hence for perfect model distinguishability. We further show that input DP is orthogonal to model confidentiality and that the local-DP premise required for shuffle amplification cannot hold under correctness-bounded noise. We recover all linear layers of a \safhire{}-style ResNet-20 end-to-end from TFHE transcripts with zero error, using $d+1$ queries per layer for a total of $5{,}712$ direct queries. Under the same query model, we also confirm exact per-layer recovery on pretrained ImageNet-scale CNNs and ViT-B/16. The leaked spectra enable fingerprinting, lineage attribution, and improved logit-based extraction, while suppressing them destroys inference utility.
    

### References & Citations

Loading...

# Bibliographic and Citation Tools

Bibliographic Explorer 

*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))
Connected Papers 

*(*[What is Connected Papers?](https://www.connectedpapers.com/about))
Litmaps 

*(*[What is Litmaps?](https://www.litmaps.co/))
scite Smart Citations 

*(*[What are Smart Citations?](https://www.scite.ai/))
# Code, Data and Media Associated with this Article

alphaXiv 

*(*[What is alphaXiv?](https://alphaxiv.org/))
CatalyzeX Code Finder for Papers 

*(*[What is CatalyzeX?](https://www.catalyzex.com))
DagsHub 

*(*[What is DagsHub?](https://dagshub.com/))
Gotit.pub 

*(*[What is GotitPub?](http://gotit.pub/faq))
Hugging Face 

*(*[What is Huggingface?](https://huggingface.co/huggingface))
ScienceCast 

*(*[What is ScienceCast?](https://sciencecast.org/welcome))
# Demos

# Recommenders and Search Tools

Influence Flower 

*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))
CORE Recommender 

*(*[What is CORE?](https://core.ac.uk/services/recommender))
# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).