Generally speaking, the more data you have, the better your machine learning model is going to be.

However, stockpiling vast amounts of data also carries a certain privacy, security, and regulatory risks.

With new privacy-preserving techniques, however, data scientists can move forward with their AI projects without putting privacy at risk.

To get the low down on privacy-preserving machine learning (PPML), we talked to Intel’s Casimir Wierzynski, a senior director in the office of the CTO in the company’s AI Platforms Group. Wierzynski leads Intel’s research efforts to “identify, synthesize, and incubate” emerging technologies for AI.

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