Can sparsity improve the privacy of neural networks?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909209544622080 |
|---|---|
| author | Gonon, Antoine Zheng, Léon Lalanne, Clément Le, Quoc-Tung Lauga, Guillaume Pouliquen, Can |
| author_facet | Gonon, Antoine Zheng, Léon Lalanne, Clément Le, Quoc-Tung Lauga, Guillaume Pouliquen, Can |
| contents | Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This article empirically investigates whether sparsity could also improve the privacy of the data used to train the networks. The experiments show positive correlations between the sparsity of the model, its privacy, and its classification error. Simply comparing the privacy of two models with different sparsity levels can yield misleading conclusions on the role of sparsity, because of the additional correlation with the classification error. From this perspective, some caveats are raised about previous works that investigate sparsity and privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07234 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Can sparsity improve the privacy of neural networks? Gonon, Antoine Zheng, Léon Lalanne, Clément Le, Quoc-Tung Lauga, Guillaume Pouliquen, Can Cryptography and Security Machine Learning Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This article empirically investigates whether sparsity could also improve the privacy of the data used to train the networks. The experiments show positive correlations between the sparsity of the model, its privacy, and its classification error. Simply comparing the privacy of two models with different sparsity levels can yield misleading conclusions on the role of sparsity, because of the additional correlation with the classification error. From this perspective, some caveats are raised about previous works that investigate sparsity and privacy. |
| title | Can sparsity improve the privacy of neural networks? |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2304.07234 |