Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving
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arXiv
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| Format: | Preprint |
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2025
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| author | Zakharov, Dmytro Kurbatov, Oleksandr Sdobnov, Artem Soukhanov, Lev Sekhin, Yevhenii Volovyk, Vitalii Velykodnyi, Mykhailo Cherepovskyi, Mark Baibula, Kyrylo Antadze, Lasha Kravchenko, Pavlo Dubinin, Volodymyr Panasenko, Yaroslav |
| author_facet | Zakharov, Dmytro Kurbatov, Oleksandr Sdobnov, Artem Soukhanov, Lev Sekhin, Yevhenii Volovyk, Vitalii Velykodnyi, Mykhailo Cherepovskyi, Mark Baibula, Kyrylo Antadze, Lasha Kravchenko, Pavlo Dubinin, Volodymyr Panasenko, Yaroslav |
| contents | In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving Zakharov, Dmytro Kurbatov, Oleksandr Sdobnov, Artem Soukhanov, Lev Sekhin, Yevhenii Volovyk, Vitalii Velykodnyi, Mykhailo Cherepovskyi, Mark Baibula, Kyrylo Antadze, Lasha Kravchenko, Pavlo Dubinin, Volodymyr Panasenko, Yaroslav Cryptography and Security Computer Vision and Pattern Recognition In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads. |
| title | Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.06784 |