---
license: mit
language:
  - en
base_model:
- openai/clip-vit-large-patch14
tags:
  - privacy
  - transformers
---

**Privacy-Preserving Split Learning via Patch Shuffling over Transformers**

Paper: https://ieeexplore.ieee.org/abstract/document/10027647
 
## API of Patch Shuffling
### PatchShuffle
function: ```utilsenc.PatchShuffle(x)->y```  
x: input feature; y: outputfeature
### BatchShuffle  
function: ```utilsenc.BatchPatchPartialShuffle(x,k1)->y```  
x: input feature; k: proportions of patches not to be shuffle; y: outputfeature
### SpectralShuffle
The function is the same as PatchShuffle or BatchShuffle, but first turn models into spectral domain. Please see the example as reference.

**Citation**
Bibtex
```
@INPROCEEDINGS{patchshuffling,
  author={Yao, Dixi and Xiang, Liyao and Xu, Hengyuan and Ye, Hangyu and Chen, Yingqi},
  booktitle={2022 IEEE International Conference on Data Mining (ICDM)}, 
  title={Privacy-Preserving Split Learning via Patch Shuffling over Transformers}, 
  year={2022},
  pages={638-647},
  doi={10.1109/ICDM54844.2022.00074}
}
```
D. Yao, L. Xiang, H. Xu, H. Ye and Y. Chen, "Privacy-Preserving Split Learning via Patch Shuffling over Transformers," 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 638-647, doi: 10.1109/ICDM54844.2022.00074.