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arXiv · 2410.20522

Props for Machine-Learning Security

Ari Juels·Farinaz Koushanfar
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Abstract

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.

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Ari Juels, Farinaz Koushanfar. 2024-10-27. Props for Machine-Learning Security. https://arxiv.org/abs/2410.20522

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