Sparsity in neural networks can improve their privacy
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917690305675264 |
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| 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 | This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_10553 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sparsity in neural networks can improve their privacy Gonon, Antoine Zheng, Léon Lalanne, Clément Le, Quoc-Tung Lauga, Guillaume Pouliquen, Can Machine Learning Artificial Intelligence Cryptography and Security This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature. |
| title | Sparsity in neural networks can improve their privacy |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2304.10553 |