Props for Machine-Learning Security

Understand

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.