Props for Machine-Learning Security
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913565576790016 |
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| author | Juels, Ari Koushanfar, Farinaz |
| author_facet | Juels, Ari Koushanfar, Farinaz |
| contents | We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML). By permitting secure use of vast sources of deep-web data, props address the systemic bottleneck of limited high-quality training data in ML development. Props also enable privacy-preserving and trustworthy forms of inference, allowing for safe use of sensitive data in ML applications. Props are practically realizable today by leveraging privacy-preserving oracle systems initially developed for blockchain applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Props for Machine-Learning Security Juels, Ari Koushanfar, Farinaz Cryptography and Security Artificial Intelligence We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML). By permitting secure use of vast sources of deep-web data, props address the systemic bottleneck of limited high-quality training data in ML development. Props also enable privacy-preserving and trustworthy forms of inference, allowing for safe use of sensitive data in ML applications. Props are practically realizable today by leveraging privacy-preserving oracle systems initially developed for blockchain applications. |
| title | Props for Machine-Learning Security |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2410.20522 |